From 0fd1323cf2143b84d87a76657704bea6ac716432 Mon Sep 17 00:00:00 2001 From: Sergio Jimenez Jimenez Date: Mon, 8 Dec 2025 11:31:26 +0100 Subject: [PATCH] Extended analysis --- src/paddle_ocr_fine_tune_unir_raytune.ipynb | 479 ++++++++++++++------ src/prepare_dataset.ipynb | 388 ++++++++-------- 2 files changed, 542 insertions(+), 325 deletions(-) diff --git a/src/paddle_ocr_fine_tune_unir_raytune.ipynb b/src/paddle_ocr_fine_tune_unir_raytune.ipynb index 6f9f1ef..c54f356 100644 --- a/src/paddle_ocr_fine_tune_unir_raytune.ipynb +++ b/src/paddle_ocr_fine_tune_unir_raytune.ipynb @@ -345,7 +345,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 16, "id": "8bfa3329", "metadata": {}, "outputs": [], @@ -357,7 +357,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 17, "id": "8bd4ca23", "metadata": {}, "outputs": [ @@ -365,9 +365,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "c:\\Users\\Sergio\\Desktop\\MastersThesis\\src\\dataset\n", - "c:\\Users\\Sergio\\Desktop\\MastersThesis\\src\\paddle_ocr_tuning.py\n", - "c:\\Users\\Sergio\\Desktop\\MastersThesis\\src\n" + "c:\\Users\\sji\\Desktop\\MastersThesis\\src\\dataset\n", + "c:\\Users\\sji\\Desktop\\MastersThesis\\src\\paddle_ocr_tuning.py\n", + "c:\\Users\\sji\\Desktop\\MastersThesis\\src\n" ] } ], @@ -2322,7 +2322,7 @@ "max 0.699247 " ] }, - "execution_count": 7, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -2334,7 +2334,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 28, "id": "50fa5b59", "metadata": {}, "outputs": [ @@ -2366,6 +2366,13 @@ " \"config/text_det_unclip_ratio\",\n", " \"config/text_rec_score_thresh\",\n", "]\n", + "labels = [\n", + " \"Detection Pixel Threshold\",\n", + " \"Detection Box Threshold\",\n", + " \"Unclip Ratio\",\n", + " \"Recognition Score Threshold\",\n", + "]\n", + "\n", "# Correlación de Pearson con CER y WER\n", "corr_cer = df[param_cols + [\"CER\"]].corr()[\"CER\"].sort_values(ascending=False)\n", "corr_wer = df[param_cols + [\"WER\"]].corr()[\"WER\"].sort_values(ascending=False)\n", @@ -2376,7 +2383,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 29, "id": "9462b7a2", "metadata": {}, "outputs": [ @@ -2413,7 +2420,7 @@ "" ] }, - "execution_count": 10, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" }, @@ -2459,45 +2466,15 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 30, "id": "02fc0a87", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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4AQAAgUJwAwAALEj+HzNTLEvoC+Q/AAAAAElFTkSuQmCC", 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SF/p43ej97RKPfxEfMWJE3DQ6ONR4UweqeqOkarvut99+Ee/9QEwn3GTsu76HHnrIlb0+N3/+/IgTRzjlPZw+o3UJ76n25ptvuhOUul//8MMPu5WrgvZ46+evY/j6qRezAnt18de2UA80BYnhHUji7U/hPf5EJ0wFcxWVc6x8xJLIsZ9oXvz9Ml5Pr0aNGlmyKN86tirTSWFPjr093X9TvV39stYP0UQ7Yqmhd/TxFmtZL7zwgv3xj390PwJ37txZbkAUq/d+ot+hbayyqqgzVrLOIfHyGo+C//CeuvFoH913331jzotepvZRnXOirxv+OVbB6Jo1a+yQQw6xPc23fqzquqeewKqw0LVPwVC0ypwHk1H+5cm4rkjaUBp24Q9/+EPM+boYVBdaV7nqqqvcr9hYok94GhdNdHJZu3ZtIL1F/Xyqt0m84Km8i3L4d0ycONENUhmLupSrNjDV4v0iq2jUlsruu+rm7J/Ely5dakVFRQnlL/qCoRO+ag81yOedd95pbdu2dTUfqrHQeEl+2Vdm/Y455hj3vc8++6z7Vfvggw+679KwChpcuDL5S3Y5V0ZFefHLRmN2KZiIlurenHty7FVVuVb1dtX+6x8L4TVne7KscPPmzbPTTjvN7dPqGacaGdV8azgcjXcWLdaPoMp+x56q7Dkk3g+2vVWnTp3dfqwmc5l1K/kd+gHqB40agkTbfezYsXb88ceHKn8qex6s6vgj44Iv/arXraCKonOlUyCii3V5v4Arc5tq//33d1GvNkD4jqdBFP35yeRX0eogTuTXiMYZ0kVQO8Z///d/u5Px22+/HXGBqOz6al39X+Q+jakTTr8CdCtAvzQqyme85fu1a/p1W953aFlKozGI9mQ5qdyuie67sm7dOnf76cQTT3QnCD8Aj5UX/RoL/6WoGlOti8btkeeff94Fcc8991xErcPe3jLTcaVbWHppvXTh0bhv8YIvf39Sfv1aRf9JFqq9SWY5V3TsJ5oXf79s0aJFQtttb/Ot4/Wnn35yx3wiKnPsVcaenCeqervq1uqECRPcuI+JBl+J0G1b1ZBpn1FQ4VPglOzv0DZWWWkQ6Hg/Mis6TyZ6DkkX2kfr1au323XDP8fqnKtAKJk05IgGNL7++uvddbGy58Egyj8tHi9UGWr/pCpDv4YnnA50jWXi3+vWL6mbbrqp3F9YGj/EfwRIRTSImwbxmz59emialnfXXXe5WhndO04mnfA1QOh9993nLsbRVJ3r0zrooqfbvGobpCBMbQ2i2wlpfRO5ty0aJ0Wib19Gj3ytXxkqb52AYgVF4fn0x2uJLvMePXq4HXvSpEkxB0X0v0MHqtoO6EDSgK3xtm285aRyuya67/ptHnSS1q1H3U5SAH3++efHrB1Qu8pwynf49vN//Yd/VvtAZS4u0dQuL5zKSbWw4bdbYpVzrP1Hv0LllFNOsWRI5NhPNC8KeBXs6zhSUFTevp2MfKv9iNpuxst3tMoce5VRmfNEUNtVNb+6va1zW6ynkqhJgn6kVJbKUBfb8NtUumVfmSefJPodOnfpHKbb9tE1LYlclypzDkkXKhv9iFQteXhTiA0bNrhawaOOOiqpt+9Fd3x++9vfunLyn6ZSmfNgEOVf5TVfalzv1yCEO/LIIyMa3yXq6quvdpGrqhY14J4u2mqUrapoDbynjav71qpuVBsUBQ76RaaDVju7qoc1T4+iEH3+1VdfdScKtRNRDYIaHca7r6xASMvVCMuqWdAydR9ZJ57KNgRNhC6s2jk1Sq8uyCoz7bTaAXRb8f3333fprrjiCndB1LpoJ9P6KhhTGwS1zenatWtofRVkqHGsRprXRVO/KGPRLzM1tFQ1unZSbbPZs2e7mpVot956q/sFobJTPtWmQTUPCgCVJ/92oQIsHRi6PaXy0k6uz6jcdVJVwKB7/6pNUVs2dSjQ9+rgVMAluhDqVpeCIm0T/dpWcKrGkGq8qe9X3lUOt912m8u7fo2q3Y4C2lRt10T3XZ0M1EhZT13w21UooNKtJQ0aeMkll0R878qVK90tD21z7ReqGVBDVn+b+7Vn2s46ISm41a9ClUWsoD4R2r76YaB1UO2SAmGtg39cxaL8qDZWwaROVNp+CxcutL///e/uoqTjMhkSOfYTzYv2O5W5vk8DOZ5zzjnul/zq1avdNvrFL34RM1jaE8OHD7dHH33UHZvKi2p3tH/o+NE2j9d5JtFjrzIqc54IaruKykf7s54AovzoNpLOIdrOGuFe+7N+wFWGgkOd/7Wf6LhRux2dd/VjIvwJJMn4Dr1XrYwGhNX21Xro3KSR2HX9Uc2eX/7a73T+1md0rOr8leg5ZE/pfBvriTLa/tqWe0rroYb7upZpX65Zs6Y75+rH2u23325VQddEnb91fGjfqMx5MJDy91Iw1ET0UAOVGWrC7/6tAfw01pK6r2o8Gg29oLF8wsdaUldVDbmg8TqUTmOPaNyfRYsWhdJ8/PHHrqurBrtLdJBVDWaoZeo7NQxE9LAJ5a3Tngyyqu65Gl5BAy5qLCoNjnjqqad6Tz/9dMRQBxprKZyGSFA+NCyAXy4ab0xDRaj7vD5TUXdyDfqoLvsaXkFdbMsbZFVlo+2l8YaUT+VXQ2Xcf//9EemUX38Avuh9QWPCaCwxLU/dz5U/DRKrMcDCabwalYm2qdKpq7CWHd6FXuNPabq6ACcyyGpF2zXemDISqzxiqWjfVdlqeACVczR1T9c20Dhg4UMYLF++3I2vpG7bGoj3sssu222Q1eeee86NT6NxjzRGlcaq8oc2CB8AON5+G11mGqRT4xhpP9Kxo2PsT3/6U8TxF2+QVXUL1/Ah2ke0r5Q3GGdF+YgnkWM/0byI9h11Sde2URlqjCONF/Tuu+8mbagJvyv8ddddF8qTjiFt2/Au+nt67PlDTUR3v/f36/D9vbLniaC2q19GOl40nICGgtH21TA1o0ePjhiM1h8MNFqs/VID0voD22qfUVnEShfvmlSZ7xAdexryQml1zGrdX3nlldB8DZmgctIxrc+Hl00i17/yzlV7MtRE+PaPV64VlY8GWdUxpG1Wr149N9hp9LiaiQzOHq6i9dQxqvO/v18keh7c2/JPRJU92xFA1VL7Kt1a062lvfm1CwAIVsa1+QIAAMhkBF8AAAAEXwAAANUTbb4AAAACxG1HAACAABF8AQAABCjjHi+0tzTY4tdff+0GzqzMIzQAAEDqeJ7nHqyuAWnjPVsyU2Rd8KXAK9nPkQIAAMFYs2ZN6OkfmSrrgi//UTHaeMl+nhQAAKgaJSUlrvKkKh7lF7SsC778W40KvAi+AADILDnVoMlQZt80BQAAyDAEXwAAAAEi+AIAAAgQwRcAAECACL4AAAACRPAFAAAQIIIvAACAABF8AQAABIjgCwAAIEBZN8I9gOqptMyzhSu/tY3bdliLhnnW+4Cmllsj80fCBlD9EHwByHizlq2zm55fbuu27ghNa5WfZ8UDC21Al1YpzRsAROO2I4CMD7wufnxxROAl67fucNM1HwDSCcEXgIy+1agaLy/GPH+a5isdAKQLgi8AGUttvKJrvMIp5NJ8pQOAdEHwBSBjqXF9MtMBQBAIvgBkLPVqTGY6AAgCwReAjKXhJNSrMd6AEpqu+UoHAOmC4AtAxtI4XhpOQqIDMP+95jPeF4B0QvAFIKNpHK+pw7pby0Z1IqbrvaYzzheAdEPwBaCaiFf3BQDpJS2CrylTpli7du0sLy/P+vTpYwsXLoyb9pFHHrGcnJyIlz4HILsHWV1fEtmjcUMJg6wCSE8pD76mT59uY8aMseLiYlu8eLF17drV+vfvbxs3boz7mUaNGtm6detCry+//DLQPANIDwyyCiATpTz4uvPOO23UqFE2cuRIKywstHvvvdfq1atn06ZNi/sZ1XYVFBSEXi1btgw0zwDSA4OsAshEKQ2+du3aZYsWLbK+ffv+J0M1arj3CxYsiPu577//3vbff39r27atDRo0yD788MOAcgwgnTDIKoBMlNLga/PmzVZaWrpbzZXer1+/PuZnOnXq5GrFnn32WXv88cetrKzMjjzySFu7dm3M9Dt37rSSkpKIF4DqgUFWAWSilN92rKyioiIbPny4devWzY499libMWOG7bPPPnbffffFTD9hwgTLz88PvVRbBqB6YJBVAJkopcFX8+bNLTc31zZs2BAxXe/VlisRtWrVssMPP9xWrFgRc/64ceNs69atodeaNWuSkncAqccgqwAyUUqDr9q1a1uPHj1s9uzZoWm6jaj3quFKhG5bLl261Fq1ahVzfp06dVzvyPAXgOo3yGpBfuSQM3rPIKsA0lHNVGdAw0yMGDHCevbsab1797bJkyfb9u3bXe9H0S3GNm3auNuHcvPNN9sRRxxhHTt2tO+++84mTpzohpq44IILUrwmAFIZgPUrLHC9H9UIX23BdEuSxwoBSEcpD76GDBlimzZtsvHjx7tG9mrLNWvWrFAj/NWrV7sekL4tW7a4oSmUtkmTJq7mbP78+W6YCgDZS4FWUYdmqc4GAFQox/M8z7KIejuq4b3af3ELEgCAzFBSja7fGdfbEQAAIJMRfAEAAASI4AsAACBABF8AAAABIvgCAAAIEMEXAABAgAi+AAAAAkTwBQAAECCCLwAAgAARfAEAAASI4AsAACBABF8AAAABIvgCAAAIEMEXAABAgAi+AAAAAkTwBQAAECCCLwAAgAARfAEAAASI4AsAACBABF8AAAABIvgCAAAIEMEXAABAgAi+AAAAAkTwBQAAECCCLwAAgAARfAEAAASI4AsAACBABF8AAAABIvgCAAAIEMEXAABAgAi+AAAAAkTwBQAAECCCLwAAgAARfAEAAASI4AsAACBABF8AAAABIvgCAAAIEMEXAABAgAi+AAAAAkTwBQAAECCCLwAAgAARfAEAAASI4AsAACBANYNcGABUldIyzxau/NY2btthLRrmWe8DmlpujRwKHEDaIfgCkPFmLVtnNz2/3NZt3RGa1io/z4oHFtqALq1SmjcAiMZtRwAZH3hd/PjiiMBL1m/d4aZrPgCkE4IvABl9q1E1Xl6Mef40zVc6AEgXBF8AMpbaeEXXeIVTyKX5SgcA6YLgC0DGUuP6ZKYDgCAQfAHIWOrVmMx0ABAEgi8AGUvDSahXY7wBJTRd85UOANIFwReAjKVxvDSchEQHYP57zWe8LwDphOALQEbTOF5Th3W3gvzIW4t6r+mM8wUg3TDIKoCMpwCrX2EBI9wDyAgEXwCqBd1aLOrQLNXZAIAKcdsRAAAg24KvKVOmWLt27SwvL8/69OljCxcuTOhzTz75pOXk5NjgwYOrPI8AAADVIviaPn26jRkzxoqLi23x4sXWtWtX69+/v23cuLHcz61atcquuuoqO/roowPLKwAAQMYHX3feeaeNGjXKRo4caYWFhXbvvfdavXr1bNq0aXE/U1paaueee67ddNNN1r59+0DzCwAAkLHB165du2zRokXWt2/f/2SoRg33fsGCBXE/d/PNN1uLFi3s/PPPr3AZO3futJKSkogXAABAVgZfmzdvdrVYLVu2jJiu9+vXr4/5mTfeeMMeeughe+CBBxJaxoQJEyw/Pz/0atu2bVLyDgAAkJG3HStj27Ztdt5557nAq3nz5gl9Zty4cbZ169bQa82aNVWeTwAAgLQc50sBVG5urm3YsCFiut4XFBTslv7zzz93De0HDhwYmlZWVub+r1mzpn3yySfWoUOHiM/UqVPHvQAAACzba75q165tPXr0sNmzZ0cEU3pfVFS0W/rOnTvb0qVLbcmSJaHXaaedZscff7z7m1uKAAAg3aV8hHsNMzFixAjr2bOn9e7d2yZPnmzbt293vR9l+PDh1qZNG9d2S+OAdenSJeLzjRs3dv9HTweQXUrLPB4vBCAjpDz4GjJkiG3atMnGjx/vGtl369bNZs2aFWqEv3r1atcDEgDimbVsnd30/HJbt3VHaFqr/DwrHljIg7UBpJ0cz/M8yyIaakK9HtX4vlGjRqnODoAkBF4XP77Yok9kOf/3/9Rh3QnAgGqgpBpdv6lSApDRtxpV4xXrF6Q/TfOVDgDSBcEXgIy1cOW3Ebcaoynk0nylA4B0QfAFIGNt3LYjqekAIAgEXwAyVouGeUlNBwBBIPgCkLF6H9DU9Wr0G9dH03TNVzoASBcEXwAyVm6NHDechEQHYP57zVc6AEgXBF8AMtqALq3ccBIF+ZG3FvWeYSYApKOUD7IKAMkIwPoVFjDCPYCMQPAFoFrQrcWiDs1SnQ0AqBC3HQEAAAJE8AUAABAggi8AAIAAEXwBAAAEiOALAAAgQARfAAAAASL4AgAACBDBFwAAQIAIvgAAAAJE8AUAABAggi8AAIAAEXwBAAAEiOALAAAgQARfAAAAASL4AgAACBDBFwAAQIAIvgAAAAJE8AUAABAggi8AAIAAEXwBAAAEiOALAAAgQARfAAAAASL4AgAACBDBFwAAQIAIvgAAAAJE8AUAABAggi8AAIAAEXwBAAAEiOALAAAgQARfAAAAASL4AgAACBDBFwAAQIAIvgAAAAJE8AUAABAggi8AAIAAEXwBAAAEiOALAAAgQARfAAAAASL4AgAACBDBFwAAQIAIvgAAAAJE8AUAABAggi8AAIAAEXwBAAAEiOALAAAgQARfAAAAASL4AgAAyLbga8qUKdauXTvLy8uzPn362MKFC+OmnTFjhvXs2dMaN25s9evXt27dutljjz0WaH4BAAAyNviaPn26jRkzxoqLi23x4sXWtWtX69+/v23cuDFm+qZNm9p1111nCxYssA8++MBGjhzpXi+99FLgeQcAAKisHM/zPEsh1XT16tXL7r77bve+rKzM2rZta6NHj7axY8cm9B3du3e3U045xW655ZYK05aUlFh+fr5t3brVGjVqtNf5BwAAVa+kGl2/U1rztWvXLlu0aJH17dv3PxmqUcO9V81WRRQ3zp492z755BM75phjYqbZuXOn22DhLwAAgKwMvjZv3mylpaXWsmXLiOl6v379+rifU9TboEEDq127tqvxuuuuu6xfv34x006YMMFFyv5LtWoAAABZ2+ZrTzRs2NCWLFli77zzjv3pT39ybcbmzp0bM+24ceNcsOa/1qxZE3h+AQAAfDUthZo3b265ubm2YcOGiOl6X1BQEPdzujXZsWNH97d6O3700Ueuhuu4447bLW2dOnXcCwAAwLK95ku3DXv06OHabfnU4F7vi4qKEv4efUZtuwAAANJdSmu+RLcMR4wY4cbu6t27t02ePNm2b9/uho+Q4cOHW5s2bVzNluh/pe3QoYMLuGbOnOnG+Zo6dWqK1wQAACADgq8hQ4bYpk2bbPz48a6RvW4jzpo1K9QIf/Xq1e42o0+B2SWXXGJr1661unXrWufOne3xxx933wMAAJDuUj7OV9Cq0zghAABki5JqdP3OyN6OAAAAmYrgCwAAIEAEXwAAAAEi+AIAAAgQwRcAAECmBl87duywSZMmJfMrAQAAsjv40phcL7zwgr388svuodjy008/2V//+ldr166d3XrrrVWRTwAAgOwbZPWNN96wU0891Y21kZOT40aaf/jhh23w4MFWs2ZNu/HGG91o9QAAAEhCzdf1119vJ598sn3wwQfusUDvvPOOnX766fbnP//Zli9fbhdddJEbdR4AAABJGOG+WbNmNm/ePCssLLQff/zRGjRoYDNmzLBBgwZZpqhOI+QCAJAtSqrR9btSNV9btmyx5s2bu79Vw1WvXj3r0qVLVeUNAACg2qn0g7V1e1EPwBZVmn3yySfuYdfhDjvssOTlEAAAIFtvO9aoUcM1tI/1EX+6/vd7Qaaj6lRtCQBAtiipRtfvStV8rVy5supyAgAAkAUqFXztv//+VZcTAACALFCpBve333676+Xoe/PNN23nzp2h99u2bbNLLrkkuTkEAADI1jZfubm5tm7dOmvRooV7r3uuS5Yssfbt27v3GzZssNatW9PmCwAAJFVJNWrzVamar+g4rRJxGwAAAJL9YG0AAACUj+ALAAAgnQdZffDBB91jheTnn3+2Rx55JDTqvRrcAwAAIEkN7tu1a+cGUc3k8cCqU4M9AACyRUk1un5XquZr1apVVZcTAACALFCpNl9z5syxwsJCF31GUyR6yCGH2Lx585KZPwAAgOwNviZPnmyjRo2KWd2nqsDf/va3dueddyYzfwAAANkbfL3//vs2YMCAuPNPPPFEW7RoUTLyBQAAUC1VKvjSCPa1atWKO79mzZq2adOmZOQLAACgWqpU8NWmTRtbtmxZ3PkffPCBtWrVKhn5AgAAqJYqFXydfPLJdsMNN9iOHTt2m6cHbhcXF9upp56azPwBAABk7zhfuu3YvXt394Dtyy67zDp16uSmf/zxxzZlyhT3QO3Fixdby5YtLV1Vp3FCAADIFiXZOs6Xgqr58+fbxRdfbOPGjQs9WFsDr/bv398FYOkceAEAAGTc44X2339/mzlzpm3ZssVWrFjhArADDzzQmjRpUjU5BAAAyObgy6dgq1evXsnNDQAAQDVXqQb3AAAA2DsEXwAAAAEi+AIAAAgQwRcAAECACL4AAAACRPAFAAAQIIIvAACAABF8AQAABIjgCwAAIEAEXwAAAAEi+AIAAAgQwRcAAECACL4AAAACRPAFAAAQIIIvAACAABF8AQAABIjgCwAAIEAEXwAAAAEi+AIAAAgQwRcAAECACL4AAAACRPAFAACQbcHXlClTrF27dpaXl2d9+vSxhQsXxk37wAMP2NFHH21NmjRxr759+5abHgAAIJ2kPPiaPn26jRkzxoqLi23x4sXWtWtX69+/v23cuDFm+rlz59rQoUPttddeswULFljbtm3txBNPtK+++irwvANIH6Vlni34/Bt7dslX7n+9B4B0lON5XkrPUKrp6tWrl919993ufVlZmQuoRo8ebWPHjq3w86Wlpa4GTJ8fPnx4helLSkosPz/ftm7dao0aNUrKOgBIrVnL1tlNzy+3dVt3hKa1ys+z4oGFNqBLq5TmDUBylFSj63dKa7527dplixYtcrcOQxmqUcO9V61WIn744Qf76aefrGnTpjHn79y5022w8BeA6hV4Xfz44ojAS9Zv3eGmaz4ApJOUBl+bN292NVctW7aMmK7369evT+g7rrnmGmvdunVEABduwoQJLlL2X6pVA1A96NaiarxiVd/70zSfW5AA0knK23ztjVtvvdWefPJJ++c//+ka68cybtw4V0Xpv9asWRN4PgFUjYUrv92txis6ANN8pQOAdFEzlQtv3ry55ebm2oYNGyKm631BQUG5n500aZILvl599VU77LDD4qarU6eOewGofjZu25HUdABQ7Wu+ateubT169LDZs2eHpqnBvd4XFRXF/dztt99ut9xyi82aNct69uwZUG4BpJsWDfOSmg4Aqn3Nl2iYiREjRrggqnfv3jZ58mTbvn27jRw50s1XD8Y2bdq4tlty22232fjx4+2JJ55wY4P5bcMaNGjgXgCyR+8DmrpejWpcH6vdV46ZFeTnuXQAkC5S3uZryJAh7haiAqpu3brZkiVLXI2W3wh/9erVtm7df3orTZ061fWSPPPMM61Vq1ahl74DQHbJrZHjhpPwA61w/nvNVzoASBcpH+craNVpnBAA/4txvoDqr6QaXb9TftsRAPaWBlLtV1jgejWqcb3aeOlWIzVeANIRwReAakGBVlGHZqnOBgCkf5svAACAbELwBQAAECCCLwAAgAARfAEAAASI4AsAACBABF8AAAABIvgCAAAIEMEXAABAgAi+AAAAAkTwBQAAECCCLwAAgAARfAEAAASI4AsAACBABF8AAAABIvgCAAAIEMEXAABAgAi+AAAAAkTwBQAAECCCLwAAgAARfAEAAASI4AsAACBABF8AAAABIvgCAAAIEMEXAABAgAi+AAAAAkTwBQAAECCCLwAAgAARfAEAAASI4AsAACBABF8AAAABIvgCAAAIEMEXAABAgAi+AAAAAkTwBQAAECCCLwAAgAARfAEAAASI4AsAACBABF8AAAABIvgCAAAIEMEXAABAgAi+AAAAAkTwBQAAEKCaQS4MAKpKaZlnC1d+axu37bAWDfOs9wFNLbdGDgUOIO0QfAHIeLOWrbObnl9u67buCE1rlZ9nxQMLbUCXVinNGwBE47YjgIwPvC5+fHFE4CXrt+5w0zUfANIJwReAjL7VqBovL8Y8f5rmKx0ApAuCLwAZS228omu8wink0nylA4B0QfAFIGOpcX0y0wFAEAi+AGQs9WpMZjoACALBF4CMpeEk1Ksx3oASmq75SgcA6YLgC0DG0jheGk5CogMw/73mM94XgHRC8AUgo2kcr6nDultBfuStRb3XdMb5ApBuGGQVQMZTgNWvsIAR7gFkBIIvANWCbi0WdWiW6mwAQPrfdpwyZYq1a9fO8vLyrE+fPrZw4cK4aT/88EM744wzXPqcnBybPHlyoHkFAADI6OBr+vTpNmbMGCsuLrbFixdb165drX///rZx48aY6X/44Qdr37693XrrrVZQUBB4fgEAADI6+Lrzzjtt1KhRNnLkSCssLLR7773X6tWrZ9OmTYuZvlevXjZx4kQ755xzrE6dOoHnFwAAIGODr127dtmiRYusb9++/8lMjRru/YIFC5K2nJ07d1pJSUnECwAAIOuCr82bN1tpaam1bNkyYrrer1+/PmnLmTBhguXn54debdu2Tdp3AwAAZFyD+6o2btw427p1a+i1Zs2aVGcJAABksZQNNdG8eXPLzc21DRs2REzX+2Q2plfbMNqHAQAAy/aar9q1a1uPHj1s9uzZoWllZWXufVFRUaqyBQAAUH0HWdUwEyNGjLCePXta79693bhd27dvd70fZfjw4damTRvXbstvpL98+fLQ31999ZUtWbLEGjRoYB07dkzlqgAAAKR/8DVkyBDbtGmTjR8/3jWy79atm82aNSvUCH/16tWuB6Tv66+/tsMPPzz0ftKkSe517LHH2ty5c1OyDgAAAJWR43meZ1lEQ02o16Ma3zdq1CjV2QEAAFl2/a72vR0BAADSCcEXAABAgAi+AAAAAkTwBQAAECCCLwAAgAARfAEAAASI4AsAACBbBlkFgGTZ9XOZPbZglX357Q+2f9N6dl5RO6tdk9+XANIPwReAjDdh5nJ7YN5KKwsbMvpPMz+yUUcfYONOLkxl1gBgNwRfADI+8Lrv3yt3m65AzJ9OAAYgnVAnDyCjbzXeP2/3wCuc5isdAKQLgi8AGevv81dZRU+n1XylA4B0QfAFIGO9s+qbpKYDgCAQfAHIWPVq1UxqOgAIAsEXgIx1UEGDpKYDgCAQfAHIWB+v35bUdAAQBIIvABnr6+9+TGo6AAgCwReAjNWmcb2kpgOAIBB8AchYZ/bYN6npACAIBF8AMtaRHZtbvdq55aapXzvXpQOAdEHwBSBj5dbIsTvP7lpumjvO7urSAUC6IPgCkNEGdGll9w7rbi0b1omYXtCojpuu+QCQThh5EEDGU4DVr7DAFq781jZu22EtGuZZ7wOaUuMFIC0RfAGoFnRrsahDs1RnAwAqxG1HAACAAFHzBaBa+HFXqf155nJb9c0P1q5ZPbv25EKrW0FPSABIBYIvABlv1KPv2CvLN4bez/vM7LG3Vlu/whb2wPBeKc0bAETjtiOAahV4hdN0zQeAdELwBSCjbzXGC7x8mq90AJAuCL4AZCy18UpmOgAIAsEXgIylxvXJTAcAQSD4ApCx1KsxmekAIAgEXwAy1jUDDk5qOgAIAsEXgIy19KutSU0HAEEg+AKQsfQcx2SmA4AgEHwByFh6gHYy0wFAEAi+AGSs3gc0tVb5eZYTZ76ma77SAUC6IPgCkLFya+RY8cBC93d0AOa/13ylA4B0QfAFIKMN6NLKpg7rbgX5kbcW9V7TNR8A0gkP1gaQ8RRg9SsssIUrv3WN69XGS7caqfECkI4IvgBUCwq0ijo0S3U2AKBC3HYEAAAIEMEXAABAgAi+AAAAAkTwBQAAECCCLwAAgADR2xFAtTD3gw32myfeDb1/5Nc97bjDWqY0TwAQC8EXgIzXbuy/dpvmArEnzFbdekpK8gQA8XDbEUC1C7wqMx8AgkbwBSCjbzUmMx0ABIHgC0DGCm/jlYx0ABAEgi8AAIAAEXwBAAAEiOALAAAgQARfADLWtHN6JDUdAGRN8DVlyhRr166d5eXlWZ8+fWzhwoXlpn/qqaesc+fOLv2hhx5qM2fODCyvANLHL7sVJDUdAGRF8DV9+nQbM2aMFRcX2+LFi61r167Wv39/27hxY8z08+fPt6FDh9r5559v7733ng0ePNi9li1bFnjeAaReRYOoMsgqgHST43mel8oMqKarV69edvfdd7v3ZWVl1rZtWxs9erSNHTt2t/RDhgyx7du32wsvvBCadsQRR1i3bt3s3nvvrXB5JSUllp+fb1u3brVGjRoleW0ApMqcJevtv55cFHGrkRovoPooqUbX75Q+XmjXrl22aNEiGzduXGhajRo1rG/fvrZgwYKYn9F01ZSFU03ZM888U+X5BZC+FGit6sajhACkv5QGX5s3b7bS0lJr2TLy4bd6//HHH8f8zPr162Om1/RYdu7c6V7hkTMAAEDWtvmqahMmTHDVlP5LtzQBAACyMvhq3ry55ebm2oYNkc9d0/uCgti9kzS9Mul1S1P3h/3XmjVrkrgGAAAAGRR81a5d23r06GGzZ88OTVODe70vKiqK+RlND08vr7zyStz0derUcQ3zwl8AAABZ2eZL1Hh+xIgR1rNnT+vdu7dNnjzZ9WYcOXKkmz98+HBr06aNu30oV1xxhR177LF2xx132CmnnGJPPvmkvfvuu3b//feneE0AAAAyIPjS0BGbNm2y8ePHu0bzGjJi1qxZoUb1q1evdj0gfUceeaQ98cQTdv3119u1115rBx54oOvp2KVLlxSuBQAAQIaM8xW06jROCAAA2aKkGl2/q31vRwAAgHRC8AUAAJBNbb6C5t9lZbBVAAAyR8n/DZJeHVpLZV3wtW3bNvc/g60CAJCZ1/H8/HzLZFnX4F7jiH399dfWsGFDy8nJsWynXxIKRDX4bKY3YExnlDPlXJ2wP1PWqeB5ngu8WrduHTEKQibKupovbbB999031dlIOwxASzlXJ+zPlHN1wz79vzK9xsuX2aEjAABAhiH4AgAACBDBV5bTsy+Li4vd/6CcMx37M+Vc3bBPV09Z1+AeAAAglaj5AgAACBDBFwAAQIAIvgAAAAJE8AUAABAggq9q7ttvv7Vzzz3XDdDXuHFjO//88+37778v9zM7duywSy+91Jo1a2YNGjSwM844wzZs2BAz7TfffOMGrdXTAr777jvLVlVRzu+//74NHTrUPYGgbt26dvDBB9tf//pXyzZTpkyxdu3aWV5envXp08cWLlxYbvqnnnrKOnfu7NIfeuihNnPmzIj56mM0fvx4a9WqlSvXvn372meffWbZLpnl/NNPP9k111zjptevX9+NSD58+HD3dJFsl+z9OdxFF13kzsWTJ0+ugpwjqdTbEdXXgAEDvK5du3pvvfWWN2/ePK9jx47e0KFDy/3MRRdd5LVt29abPXu29+6773pHHHGEd+SRR8ZMO2jQIO+kk05Sj1lvy5YtXraqinJ+6KGHvMsvv9ybO3eu9/nnn3uPPfaYV7duXe+uu+7yssWTTz7p1a5d25s2bZr34YcfeqNGjfIaN27sbdiwIWb6N99808vNzfVuv/12b/ny5d7111/v1apVy1u6dGkoza233url5+d7zzzzjPf+++97p512mnfAAQd4P/74o5etkl3O3333nde3b19v+vTp3scff+wtWLDA6927t9ejRw8vm1XF/uybMWOGOwe1bt3a+8tf/hLA2mBvEHxVYzpYFRS98847oWkvvviil5OT43311VcxP6OTpg7up556KjTto48+ct+jE2i4e+65xzv22GNd8JDNwVdVl3O4Sy65xDv++OO9bKEL9qWXXhp6X1pa6i4uEyZMiJn+7LPP9k455ZSIaX369PF++9vfur/Lysq8goICb+LEiRHbok6dOt7//M//eNkq2eUcy8KFC93+/eWXX3rZqqrKee3atV6bNm28ZcuWefvvvz/BVwbgtmM1tmDBAncLrGfPnqFpusWi51u+/fbbMT+zaNEid8tA6Xyq8t5vv/3c9/mWL19uN998sz366KMZ/4DTdC7naFu3brWmTZtaNti1a5crp/AyUpnqfbwy0vTw9NK/f/9Q+pUrV9r69esj0uhZcbr9U165V2dVUc7x9l3dEtOxko2qqpzLysrsvPPOs6uvvtoOOeSQKlwDJFN2XzWrOV1kWrRoETGtZs2a7uKtefE+U7t27d1OkC1btgx9ZufOna4t0sSJE12wkO2qqpyjzZ8/36ZPn24XXnihZYPNmzdbaWmpK5NEy0jTy0vv/1+Z76zuqqKcY7VvVBswnTfULjIbVVU533bbbe58c/nll1dRzlEVCL4y0NixY90vyPJeH3/8cZUtf9y4ca7x97Bhw6w6S3U5h1u2bJkNGjTIPQrqxBNPDGSZQDKohvfss892HR2mTp1KoSaRatLUCeeRRx5x5yNkjpqpzgAq78orr7Tf/OY35aZp3769FRQU2MaNGyOm//zzz65nnubFoumqHlfPxfBaGfXC8z8zZ84cW7p0qT399NPuvf+EqubNm9t1111nN910U7XYrKku5/BbvCeccIKr8br++ustW2h/ys3N3a2nbawy8ml6een9/zVNvR3D03Tr1s2yUVWUc3Tg9eWXX7rzRrbWelVVOc+bN8+de8LvQKh2Tecu9XhctWpVlawLkiDVjc5Q9Q3B1ZPO99JLLyXUEPzpp58OTVNvpfCG4CtWrHC9bfyXeu5o/vz58+P22qnOqqqcRQ1oW7Ro4V199dVetjZQvuyyyyIaKKthcXkNlE899dSIaUVFRbs1uJ80aVJo/tatW2lwn+Ryll27dnmDBw/2DjnkEG/jxo2V3vbVUbLLefPmzRHnYr3UgP+aa65x5xOkL4KvLBgC4fDDD/fefvtt74033vAOPPDAiCEQ1EumU6dObn74EAj77befN2fOHBdQ6GDXK57XXnstq3s7VlU560S6zz77eMOGDfPWrVsXemXThUxd89UT8ZFHHnFB7oUXXui65q9fv97NP++887yxY8dGdM2vWbOmC67Ue7S4uDjmUBP6jmeffdb74IMP3HApDDWR3HJW4KUhPPbdd19vyZIlEfvvzp07vWxVFftzNHo7ZgaCr2rum2++cUFAgwYNvEaNGnkjR470tm3bFpq/cuVKFzgpgPJpvCMNadCkSROvXr163umnn+5OmvEQfFVNOetEq89Ev3RyzSYa10xBqsZHUs2BxlLzaaiTESNGRKT/f//v/3kHHXSQS69al3/9618R81X7dcMNN3gtW7Z0F8ITTjjB++STT7xsl8xy9vf3WK/wYyAbJXt/jkbwlRly9E8ybl8CAACgYvR2BAAACBDBFwAAQIAIvgAAAAJE8AUAABAggi8AAIAAEXwBAAAEiOALAAAgQARfAPaIHuYb/lzKbKSHGT/zzDOBLlPP69NylyxZslff065dO/f8v3RbPyAbEHwBKaaHd+sip1etWrWsZcuW1q9fP5s2bZqVlZVV6rtuvPHGKnlAdKwL9ZAhQ+zTTz+1qnbccceFykcvlc9ZZ53lHtYc1DKjX5oPAHuK4AtIAwMGDLB169a5Wo0XX3zRjj/+eLviiivs1FNPtZ9//tnSUd26da1FixaBLGvUqFGufL7++mt79tlnbc2aNTZs2LAqW96MGTPc8vRauHChm/bqq6+Gpmn+ntADRdJ1ewIIDsEXkAbq1KljBQUF1qZNG+vevbtde+21LshQIKbbe77vvvvOLrjgAttnn32sUaNG9stf/tLef/99N0/pbrrpJvfer6HxP1ve53zPP/+89erVy/Ly8qx58+Z2+umnu+mq5VEt0+9///vQ98a77Th16lTr0KGD1a5d2zp16mSPPfZYxHx99sEHH3TfXa9ePTvwwAPtueeeq7B8lFbl06pVKzviiCPssssus8WLF0ekef311613796uLJVu7NixoUDn0UcftQYNGthnn30WSn/JJZdY586d7YcffthteU2bNnXL00tlJs2aNQtN03zf5s2b467P3Llz3TprO/bo0cPl7Y033nA1mhMmTLADDjjABbFdu3a1p59+OvS5LVu22LnnnuuWrfn63ocffjgij1988YUL0rVcfX7BggUR8//xj3/YIYcc4papmss77rij3DJW2RxzzDFu+xcWFtorr7xS4XYBsIdS/XBJINvpQbqDBg2KOa9r167eSSedFHrft29fb+DAgd4777zjffrpp96VV17pNWvWzD3Y+4cffnDv9fBdPaBbL02r6HPywgsveLm5ud748eO95cuXe0uWLPH+/Oc/u3lKs++++3o333xz6Hvl4Ycf9vLz80N5mzFjhlerVi1vypQp7kHVd9xxh/vOOXPmhNLolKPveuKJJ7zPPvvMu/zyy93DyP18xKKHDV9xxRWh90qrdTn++OND09auXeseTq4HlX/00UfeP//5T6958+bu4eS+s846y+vVq5f3008/ufVVXt99990Kt4//kOj33ntvt3kVrY//0PnDDjvMe/nll70VK1a4eX/84x+9zp07e7NmzfI+//xzV5Z6yPfcuXPd5y699FKvW7dubntp+a+88or33HPPReRHn9d6qKzPPPNM90BlrZtovWrUqOG2mebr++vWrev+j/UA5tLSUq9Lly7uIePa9q+//rp3+OGHu+WoLAEkF8EXkMbB15AhQ7yDDz7Y/T1v3jyvUaNG3o4dOyLSdOjQwbvvvvvc3wo2FLCFS+RzRUVF3rnnnhs3j+EXal908HXkkUd6o0aNikijgOfkk08OvdfF/Prrrw+9//777920F198sdzgS4FS/fr1XYCl9AcddJALQnzXXnut16lTJ6+srCw0TUGgAiEFFvLtt9+6QOniiy/2WrZs6f3pT3/yElFR8FXe+vjB1zPPPBNKo+2g9Zg/f37Ed51//vne0KFD3d8KLkeOHFlufh588MHQtA8//NBNU+Apv/71r71+/fpFfO7qq6/2CgsLY27Tl156yatZs6b31VdfheZrHQi+gKrBbUcgjen67t/m023C77//3t3+0i00/7Vy5Ur7/PPP435HIp9Tz7kTTjhhr/L60Ucf2S9+8YuIaXqv6eEOO+yw0N/169d3t0E3btxY7nfrFpzyqHXRbbuOHTvaiSeeaNu2bQstu6ioKFRW/rK13mvXrnXvmzRpYg899FDo1qhuSyZDIuvTs2fP0N8rVqxwtzrVqSJ8e+jWqL89Lr74YnvyySdd54k//OEPNn/+/HKXq9us4i833rbQrcXS0tLdvkvp27Zta61btw5NU3kCqBo1q+h7ASSBLopqFyQKJHSRVTuiaOUN+ZDI59SuKCjq0RlOAVNFvTrz8/NdwCX6X0GU1mn69OmuLVui/v3vf1tubq5rNL99+3Zr2LChBbE+CsrCt4f861//cm38wql9lpx00kmund3MmTNd2ysFxpdeeqlNmjQp5nL9oLOyvWMBpAY1X0CamjNnji1dutTOOOMM914N8devX281a9Z0AUj4Sw3kRQ3do2s2EvmcalFmz54dNy+xvjfawQcfbG+++WbENL1X4+1kUwAlP/74Y2jZanD+v3cC/7NsBVf77ruve6/ao9tuu811LFBNkxrtp4LKQ0HW6tWrd9seqn3yqbH9iBEj7PHHH3fDfNx///0JLyPetjjooINCZRedXj1IFZT63nrrrT1eRwDlo+YLSAM7d+50AZICnA0bNtisWbNcbzgNNTF8+HCXpm/fvu5W0ODBg+322293F1INvaAaFPW2060t9WrT7UTdolPQoeAjkc8VFxe72hXdjjvnnHNcL0HVulxzzTVu2fpe1RppngIHP2gLd/XVV9vZZ59thx9+uFumghwNyaAhGvaWbtOpfETlc8stt7heebr16PdcVIAyevRoF1R98sknbp3GjBljNWrUcLcnzzvvPLv88stdrZLKRj07Bw4caGeeeaYFSdvkqquucr1HVVN11FFH2datW11wpFuWCrjGjx/vekeqt6L2jRdeeMEFSIm68sor3fqpnDQemwLTu+++2+65556Y6bW9tF9o2RMnTrSSkhK77rrrkrjWACJUUVsyAJVocK9DUS81et5nn31c78Rp06aFGov7SkpKvNGjR3utW7d2jdDbtm3rGsqvXr061Jj7jDPO8Bo3buy+z+/dVtHn5B//+IfrYVe7dm3XU/BXv/pVaN6CBQtcjz31yPNPG9EN7uWee+7x2rdv75ahRvGPPvpoxPxYDbj1HeG98GI1uPfLR68mTZq4aeG9KEU9BdWbUfkvKCjwrrnmmlDvPzVeP/TQQyM6Hag3ZtOmTV1Pyb1pcF/e+vgN7rds2RKRRh0DJk+e7DoJqKy0zfv37+96Gcott9ziOlqoh6LyqA4ZX3zxRdz86Ps1TcvzPf30066Bvb5/v/328yZOnFhuJwr1ijzqqKNc+WnbqScmDe6BqpGjfyLDMQAAAFQV2nwBAAAEiOALAAAgQARfAAAAASL4AgAACBDBFwAAQIAIvgAAAAJE8AUAABAggi8AAIAAEXwBAAAEiOALAAAgQARfAAAAASL4AgAAsOD8fwmFacFWnIU4AAAAAElFTkSuQmCC", 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", - "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -2505,75 +2482,30 @@ } ], "source": [ - "import matplotlib.pyplot as plt\n", + "fig, axes = plt.subplots(2, 2, figsize=(12, 10))\n", "\n", - "plt.scatter(df[\"config/text_det_thresh\"], df[\"CER\"])\n", - "plt.xlabel(\"Detection Box Threshold\")\n", - "plt.ylabel(\"CER\")\n", - "plt.title(\"Effect of Detection pixel threshold on Character Error Rate\")\n", - "plt.show()\n", + "for ax, col, label in zip(axes.flat, param_cols, labels):\n", + " ax.scatter(df[col], df[\"CER\"], alpha=0.6)\n", + " ax.set_xlabel(label)\n", + " ax.set_ylabel(\"CER\")\n", + " ax.set_title(f\"Effect of {label} on CER\")\n", "\n", - "plt.scatter(df[\"config/text_det_box_thresh\"], df[\"CER\"])\n", - "plt.xlabel(\"Detection Box Threshold\")\n", - "plt.ylabel(\"CER\")\n", - "plt.title(\"Effect of Detection box threshold on Character Error Rate\")\n", - "plt.show()\n", - "\n", - "plt.scatter(df[\"config/text_det_unclip_ratio\"], df[\"CER\"])\n", - "plt.xlabel(\"Detection Box Threshold\")\n", - "plt.ylabel(\"CER\")\n", - "plt.title(\"Effect of Text detection expansion coefficient on Character Error Rate\")\n", - "plt.show()\n", - "\n", - "plt.scatter(df[\"config/text_rec_score_thresh\"], df[\"WER\"])\n", - "plt.xlabel(\"Line Tolerance\")\n", - "plt.ylabel(\"WER\")\n", - "plt.title(\"Effect of Text recognition threshold on Character Error Rate\")\n", - "plt.show()\n", - "\n" + "plt.tight_layout()\n", + "plt.savefig(\"hyperparameter_analysis_cer.png\", dpi=150)\n", + "plt.show()" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 31, "id": "cc1e3d53", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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", 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JobVYiYrcr9VNwW9kkJ/MtCW6DgV1/f8blCkI135Uv0V1DPdvuHXzpU7Mhx56qD344IMuyFSQq1pF9fWLp0Nwpl+TCG6yhE4Cqu6v6E5Cy+nkq5NJebU3iTRRtWnTxkXoKsyhB7FGM/nzk8m/u9KdVjx3Tmq2UYdZHWQamaDA45///GfYxamyTXKhdIeiE7buguLZD0pD5PM8Qinf1FSgprfQ2pvIfNVf5b1GooXejaqGK5kysUwkY7/F4neiV0freMqZllftjV66a1UHcFX3T506NaHvVd6oI2+kivLOn+7XOIWKtr5Yn9eyoTUYCi5UtuLJg3jpgqoaLtUOKIhRTU3oM1YU6EybNq3sJsUPbnSMqUNurPxRWauoQ3BoPoXWyulY1HZGe8BgdUnG9iVq5MiRbmj+bbfdVtbErc7Du3fvdqNPQ2sb/abYeI7BeK9J6UKfmyyh/ieqPvRrKEKpqUQ99kUjKXRnpRER5d1xqcd/5PDPWHTnpaGwOhn59H0aoaR2+WhPHK0KXWw0ckAjCNTPIJKqdn3aBn/klx5MqCBH1fP6fyhtbzztyBWdsJW/6k+jEU/lpUvLqf1Z/YFi7Qf/QVuRy+jOSScUf0SKX3sV+YwK5X8y+c/PiKdcVFeZSCRNiVK+6qKrsqILX6z9qSp7VfFHntgVkOoCkSjl3bx588KeGqtRMGrq1GiaWP1n1C9DAZVGVoWWZfUDidVPJ5QuQro7VzNF6LnAf+BeeaPvKkMBi/JQQ45VUxMaBCu40QX+hRdeKBtB6h9jP/rRj9z00GH2GmGmEXdaZ0XNqqqdUBDx2GOPucDNpxGkqShHiUjG9iWqYcOGbjSTrh3qs+WnQ0LLgcqA9lWkWNeKeK9J6ULNTYqoM6h/JxZKB3Ui7b4+DQ9WlK1qRn9oqU6IixYtckM0daCojVhVj+pQqxOY7lw0lFN31xoKrnkakij6vGoNVCWpfh9q143WD8Bvw1Wgoe9V+7hOwPpO3ZVpuF9VH7wVq0O2DnTd7alDnfJMJwAdTGp2+ve//+2W05BjdZTVtuiA1fYq2FH7vjoV+3dp2l5diIcNG+aesaILsJoZEjVu3Dh3d6O8Urp0IVItmQIqpUH/F/XH0PNN9H26kKnjnfaXllHHYKVN3699ojsr7T+lVZ0NdeJTM4hfs6C0K1hSXmtb/aHgejZIMms3dOFUHqp/iU50qm73n4ORrjKRSJoSpYuIhsPqeNGDzzRkXxdFDe9W51p1fFXgqXxWFb5O5trfqhHUs2VUHvWZRKmJ4Nlnn3XBqzpgq4ZVAYtqFRQ4x2riEDWFKQjRsaFnLam8KaBUHw3dRZdH26Zhurrx0XGiIcAKMBQ065hI9gM0/doYHbOhz2IS/3EKejyFjoPQMqxjVwGbPq9jRfmtsqZAMp7n7KimVOvQBV1lRf15lLe6cCdy7tWghWi1cjp3VOXJ7lXdvsq44YYb3HGp89dzzz3nAiwFusp75ZPKjmp3dFxF3lDq/KPjROlWTZuWUb7Ge01Km7SO1cqxoeCRwzoTGQruDxcdMWKE17FjRzeUsGnTpm5o9P3331825FI0bFDDVg899FC33AEHHOD9+Mc/dkOKfR999JEbdqjhmvq+ioaFr1+/3hs6dKj7Tq1Tw7RDt6WibUp0KLhoqOTgwYPd8NNatWp5rVq18k477TRv+vTpYcOJH3jggbDPaRiy0tG1a9eyfNm+fbt34YUXuqG/8QybjbUP/LzQvNatW7t0KX0auq6hppFDLUeOHOmGwPrLaZh36BBQ7dObbrrJa9mypVtGw9y170KH7cuOHTvcdzZu3NgNtdZw0uXLl7t0jhs3bp+h4MrTaOVSw0VjDQWXSZMmuaGgGsoZOgQ7cih4vGXCH54abRh1tH0eTaw0xSprkWn1t/3999+Pun6tT0NgNfy7oKDA69Chgxtm/8EHH7j5GvavvNfxpKGvWq53797e888/H7aeeNMjKgMqCyqP+s5evXp5f/3rX8OWiTYUXP70pz95hx12mBti3qlTJ2/GjBluP8b7qAMN/da2qLw1b97cu/rqq91w98g0awhwpES+R2W2Zs2abhs0XD1Sly5d3Lx77rlnn3kLFixw+0RlXY95OPHEE7133303bJmK9quGnOvYUz716NHDDdGPti8SHQoeuv3KD5WJRM8hydi+SOUda6IyrWPIf8TEiy++6PaByp8eQaD94D/CIvQ8oSHlKtcaqh45lD7ea1I65Omf9IVWACpLVczqzKq7S/+pqAAA+twAWUHPDImkamY1YehJvgCA79HnBsgCaotX3xb10VEbvfp06aW+L8keXQEA2Y5mKSALqAOiOoJqVIw6/2n4pjrCqjNyeQ8KBIBcRHADAAAChefcAACAQCG4AQAAgZJzjfV6oN0XX3zhHjKWyke7AwCA5NGTa/RzNXrwbHkPu8zJ4EaBDaNLAADITvrl9Fi/xJ6zwY3/WHhlTrJ/wwMAAKTG1q1bXeVEPD/vknPBjd8UpcCG4AYAgOwST5cSOhQDAIBAIbgBAACBQnADAAACheAGAAAECsENAAAIFIIbAAAQKGkPbiZOnGht27a1goIC6927t82bN6/c5bds2WLXXnuttWjRwurUqWMHH3ywvfLKK9WWXgAAkNnS+pybadOm2bBhw+yxxx5zgc348eNt4MCBtnz5cmvWrNk+y+/Zs8cGDBjg5k2fPt1atWpln332mTVs2DAt6QcAAJknz9OPNaSJApqePXvahAkTyn73SU8fvP7662348OH7LK8g6L777rOPPvrIatWqVeknHBYWFlpJSQkP8QMAIEskcv1OW7OUamHmz59v/fv3/z4xNWq493Pnzo36mRdffNH69OnjmqWaN29unTt3trvvvttKS0tjfs/u3btdhoS+gOpQutezuSu/tBcWrnV/9R4AEOBmqU2bNrmgREFKKL1XzUw0n3zyib355pt20UUXuX42K1assGuuuca++eYbGz16dNTPjB071saMGZOSbQBimbl4nY15aamtK9lVNq1FYYGNPr2Tndy5BRkHAEHuUJwINVupv80TTzxh3bt3t0GDBtnIkSNdc1UsI0aMcFVY/ks/mAmkOrC5euqCsMBGikt2uemaDwAIYM1N06ZNLT8/39avXx82Xe+LioqifkYjpNTXRp/zHXbYYVZcXOyauWrXrr3PZzSiSi+gOqjpSTU20RqgNE0/96b5AzoVWX6Nin/8DQCQRTU3CkRU+zJr1qywmhm9V7+aaI499ljXFKXlfB9//LELeqIFNkB1m7dq8z41NpEBjuZrOQBAAJulNAx80qRJ9swzz9iyZcvs6quvth07dtjQoUPd/MGDB7tmJZ/mb9682W644QYX1Lz88suuQ7E6GAOZYMO2XUldDgCQZc+5UZ+ZjRs32qhRo1zTUrdu3WzmzJllnYxXr17tRlD5NEz81VdftZtuusm6dOninnOjQOfWW29N41YA32tWvyCpywEAsuw5N+nAc26Q6j43x93zpus8HO3AUi+bosICm3PrSfS5AYCgPecGCCJ1EtZwb4nsLuy/13w6EwNA6hDcAEmm59g8evFRroYmlN5rOs+5AYAA97kBgkoBjIZ7a1SUOg+rj02vdo2psQGAakBwA6SImp76dGhC/gJANaNZCgAABArBDQAACBSCGwAAECgENwAAIFAIbgAAQKAQ3AAAgEBhKDgQ588q8MwaAMgOBDdABWYuXmdjXlpq60q+/yXvFoUF7mcUeNowAGQemqWACgKbq6cuCAtsRD+MqemaDwDILAQ3QDlNUaqxifbr3v40zddyAIDMQXADxKA+NpE1NqEU0mi+lgMAZA6CGyAG/eBlMpcDAFQPghsgBv2SdzKXAwBUD4IbIIZe7Rq7UVF5MeZruuZrOQBA5iC4AWLIr5HnhntLZIDjv9d8LQcAyBwEN0A59BybRy8+yooKw5ue9F7Tec4NAGQeHuIHVEABzIBORTyhGACyBMENEAc1PfXp0IS8AoAsQLMUAAAIFIIbAAAQKAQ3AAAgUAhuAABAoBDcAACAQCG4AQAAgUJwAwAAAoXgBgAABArBDQAACBSCGwAAECgENwAAIFAIbgAAQKAQ3AAAgEAhuAEAAIFCcAMAAAKF4AYAAAQKwQ0AAAgUghsAABAoBDcAACBQCG4AAECgENwAAIBAIbgBAACBQnADAAACheAGAAAECsENAAAIFIIbAAAQKAQ3AAAgUAhuAABAoBDcAACAQCG4AQAAgUJwAwAAAqVmuhMAAECmKN3r2bxVm23Dtl3WrH6B9WrX2PJr5KU7WUgQwQ0AAGY2c/E6G/PSUltXsqssP1oUFtjo0zvZyZ1bkEdZhGYpAEDOU2Bz9dQFYYGNFJfsctM1H9mD4AYAYLneFKUaGy/KPH+a5ms5ZIeMCG4mTpxobdu2tYKCAuvdu7fNmzcv5rJTpkyxvLy8sJc+BwBAZaiPTWSNTSiFNJqv5ZAd0h7cTJs2zYYNG2ajR4+2BQsWWNeuXW3gwIG2YcOGmJ9p0KCBrVu3ruz12WefVWuaAQDBoc7DyVwO6Zf24ObBBx+0K664woYOHWqdOnWyxx57zOrWrWuTJ0+O+RnV1hQVFZW9mjdvXq1pBgAEh0ZFJXM55Hhws2fPHps/f77179//+wTVqOHez507N+bntm/fbm3atLHWrVvbmWeeaUuWLIm57O7du23r1q1hLwAAfBrurVFRsQZ8a7rmazlkh7QGN5s2bbLS0tJ9al70vri4OOpnDjnkEFer88ILL9jUqVNt7969dswxx9jnn38edfmxY8daYWFh2UsBEQAAPj3HRsO9JTLA8d9rPs+7yR5pb5ZKVJ8+fWzw4MHWrVs369evn82YMcMOOOAAe/zxx6MuP2LECCspKSl7rVmzptrTDADIbHqOzaMXH2VFheFNT3qv6TznJruk9SF+TZs2tfz8fFu/fn3YdL1XX5p41KpVy4488khbsWJF1Pl16tRxLwAAyqMAZkCnIp5QHABprbmpXbu2de/e3WbNmlU2Tc1Meq8amnioWWvRokXWogVPjwQAVI2anvp0aGJndmvl/tIUlZ3S/vMLGgY+ZMgQ69Gjh/Xq1cvGjx9vO3bscKOnRE1QrVq1cn1n5I477rCjjz7aOnbsaFu2bLH77rvPDQW//PLL07wlAAAgE6Q9uBk0aJBt3LjRRo0a5ToRqy/NzJkzyzoZr1692o2g8n311Vdu6LiWbdSokav5effdd90wcgAAgDzP83LqedIaCq5RU+pcrIcBAgCAYF2/s260FAAAQHkIbgAAQKAQ3AAAgEAhuAEAAIFCcAMAAAKF4AYAAAQKwQ0AAAgUghsAABAoBDcAACBQCG4AAECgENwAAIBAIbgBAACBQnADAAACheAGAAAECsENAAAIFIIbAAAQKAQ3AAAgUAhuAABAoNRMdwIAAKiK0r2ezVu12TZs22XN6hdYr3aNLb9GHpmawwhuAABZa+bidTbmpaW2rmRX2bQWhQU2+vROdnLnFmlNG9KHZikAQNYGNldPXRAW2EhxyS43XfORmwhuAABZ2RSlGhsvyjx/muZrOeQeghsAQNZRH5vIGptQCmk0X8sh9xDcAACyjjoPJ3M5BAvBDQAg62hUVDKXQ7AQ3AAAso6Ge2tUVKwB35qu+VoOuYfgBgCQdfQcGw33lsgAx3+v+TzvJjcR3AAAspKeY/PoxUdZUWF405PeazrPucldPMQPAJC1FMAM6FTEE4oRhuAGAJDV1PTUp0OTdCcDGYRmKQAAECgENwAAIFAIbgAAQKAQ3AAAgEAhuAEAAIFCcAMAAAKF4AYAAAQKwQ0AAAgUghsAABAoBDcAACBQCG4AAECgENwAAIBAIbgBAACBQnADAAACheAGAAAECsENAAAIFIIbAAAQKAQ3AAAgUAhuAABAoBDcAACAQCG4AQAAgUJwAwAAAoXgBgAABArBDQAACBSCGwAAECgENwAAIFAIbgAAQKBkRHAzceJEa9u2rRUUFFjv3r1t3rx5cX3uueees7y8PDvrrLNSnkYAAJAd0h7cTJs2zYYNG2ajR4+2BQsWWNeuXW3gwIG2YcOGcj/36aef2i233GJ9+/attrQCAIDMl/bg5sEHH7QrrrjChg4dap06dbLHHnvM6tata5MnT475mdLSUrvoootszJgx1r59+2pNLwAAyGxpDW727Nlj8+fPt/79+3+foBo13Pu5c+fG/Nwdd9xhzZo1s8suu6zC79i9e7dt3bo17AUAAIIrrcHNpk2bXC1M8+bNw6brfXFxcdTPzJkzx5566imbNGlSXN8xduxYKywsLHu1bt06KWkHAACZKe3NUonYtm2bXXLJJS6wadq0aVyfGTFihJWUlJS91qxZk/J0AgCA9KmZxu92AUp+fr6tX78+bLreFxUV7bP8ypUrXUfi008/vWza3r173d+aNWva8uXLrUOHDmGfqVOnjnsBAIDckNaam9q1a1v37t1t1qxZYcGK3vfp02ef5Q899FBbtGiRLVy4sOx1xhln2Iknnuj+T5MTAABIa82NaBj4kCFDrEePHtarVy8bP3687dixw42eksGDB1urVq1c3xk9B6dz585hn2/YsKH7GzkdAADkprQHN4MGDbKNGzfaqFGjXCfibt262cyZM8s6Ga9evdqNoAIAAIhHnud5nuUQDQXXqCl1Lm7QoEG6kwMAAJJ8/aZKBAAABArBDQAACBSCGwAAECgENwAAIFAIbgAAQKAQ3AAAgEAhuAEAAIFCcAMAAAKF4AYAAAQKwQ0AAAgUghsAABAoBDcAACBQCG4AAECgENwAAIBAIbgBAACBkrTgZteuXXb//fcna3UAAACpD242btxof/3rX+21116z0tJSN+2bb76x3/zmN9a2bVsbN25c5VIBAACQJDXjXXDOnDl22mmn2datWy0vL8969OhhTz/9tJ111llWs2ZNu/32223IkCHJShcAAEBqa25uu+02O+WUU+zDDz+0YcOG2fvvv29nn3223X333bZ06VK76qqrbL/99qtcKgAAAJIkz/M8L54FmzRpYm+//bZ16tTJvv76a6tXr57NmDHDzjzzTMsmqnkqLCy0kpISa9CgQbqTAwAAknz9jrvm5quvvrKmTZu6/6uGpm7duta5c+d4Pw4AAJBZfW5EzU/FxcXu/6rwWb58ue3YsSNsmS5duiQ3hQAAAKlolqpRo4brSBxtcX+6/vqjqDIVzVIAAGSfRK7fcdfcrFq1KhlpAwAASKm4g5s2bdqkNiUAAABJEHeH4nvvvdeNkvK98847tnv37rL327Zts2uuuSYZaQIAAEh9n5v8/Hxbt26dNWvWzL1Xe9fChQutffv27v369eutZcuW9LkBAADZMRQ8MgaKMyYCAACoVvwqOAAACBSCGwAAkLsP8XvyySfdzy7It99+a1OmTCl7arE6FAMAAGRNh+K2bdu6h/Rl+/NweIgfAADZJyUP8XvrrbesXbt2yUgfAABA+vvcdOjQwQU3P/3pT23q1Km2du3a1KUKAACgkuKuuXnzzTdt9uzZ7vXss8/anj173DNuTjrpJDvxxBPdq3nz5pVNBwAAQPX2uQm1a9cue/fdd8uCnXnz5tk333xjhx56qC1ZssQyGX1uAADIPolcvysV3PhUe6OfYfjb3/5mjz/+uG3fvp0nFAMAgOzoUOwHM++9957rXKwam3/+85/WunVrO/74423ChAnWr1+/qqYdAACgSuIObtS3RsGMOhUriLnyyivtD3/4g7Vo0aJqKQAAAEhHcPP222+7QEZBzgknnOACnCZNmiQzLQAAANU3FHzLli32xBNPWN26de2ee+5xvwB+xBFH2HXXXWfTp0+3jRs3Vj01AAAAVVTpDsX6uYU5c+aU9b/597//bQcddJAtXrzYMhmjpQAAyD6JXL8r/cOZ+++/vzVu3Ni9GjVqZDVr1rRly5ZVdnUAAADV2+dm79699sEHH7haGtXWaAj4jh07rFWrVu4BfhMnTnR/AQAAsiK4adiwoQtmioqKXBDz0EMPuY7F+lkGAACArAtu7rvvPhfUHHzwwalNEQAAQHUEN3quDQAAQKardIdiAACATERwAwAAAoXgBgAABArBDQAACBSCGwAAECgENwAAIFAIbgAAQKAQ3AAAgEAhuAEAAIGSEcGNfnSzbdu2VlBQYL1797Z58+bFXHbGjBnWo0cP91tX+mXybt262e9///tqTS8AAMhcaQ9upk2bZsOGDbPRo0fbggULrGvXrjZw4EDbsGFD1OUbN25sI0eOtLlz59qHH35oQ4cOda9XX3212tMOAAAyT57neV46E6Camp49e9qECRPc+71791rr1q3t+uuvt+HDh8e1jqOOOspOPfVUu/POOytcduvWrVZYWGglJSXWoEGDKqcfAACkXiLX77TW3OzZs8fmz59v/fv3/z5BNWq496qZqYjislmzZtny5cvt+OOPT3FqAWBfpXs9m7vyS3th4Vr3V+8BZMmvgqfCpk2brLS01Jo3bx42Xe8/+uijmJ9T1NaqVSvbvXu35efn2yOPPGIDBgyIuqyW0Ss08gOAZJi5eJ2NeWmprSvZVTatRWGBjT69k53cuQWZDORqn5vKqF+/vi1cuNDef/99+/Wvf+367MyePTvqsmPHjnXVWP5LTV4AkIzA5uqpC8ICGyku2eWmaz6AHAxumjZt6mpe1q9fHzZd74uKimJ+Tk1XHTt2dCOlbr75Zjv33HNdEBPNiBEjXE2P/1qzZk3StwNAblHTk2psojVA+dM0nyYqIAeDm9q1a1v37t1dvxmfOhTrfZ8+feJejz4T2vQUqk6dOq7jUegLAKpi3qrN+9TYRAY4mq/lAORYnxtRk9KQIUPcs2t69epl48ePtx07drjh3TJ48GDXv8avmdFfLduhQwcX0LzyyivuOTePPvpomrcEQK7YsG1XUpcDELDgZtCgQbZx40YbNWqUFRcXu6ammTNnlnUyXr16tWuG8inwueaaa+zzzz+3/fbbzw499FCbOnWqWw8AVIdm9QuSuhyAgD3nprrxnBsAVaW+NMfd86brPBztBJpnZkWFBTbn1pMsv4beAciZ59wAQDZSwKLh3hIZuvjvNZ/ABkgPghsAqAQ9x+bRi49yNTSh9F7Tec4NkMN9bgAgWymAGdCpyI2KUudh9bHp1a4xNTZAmhHcAEAVqOmpT4cm5CGQQWiWAgAAgUJwAwAAAoXgBgAABArBDQAACBSCGwAAECgENwAAIFAIbgAAQKAQ3AAAgEAhuAEAAIFCcAMAAAKF4AYAAAQKwQ0AAAgUghsAABAoBDcAACBQCG4AAECgENwAAIBAIbgBAACBQnADAAACheAGAAAECsENAAAIFIIbAAAQKAQ3AAAgUAhuAABAoBDcAACAQCG4AQAAgUJwAwAAAoXgBgAABArBDQAACBSCGwAAECgENwAAIFAIbgAAQKAQ3AAAgEAhuAEAAIFCcAMAAAKF4AYAAAQKwQ0AAAgUghsAABAoNdOdAADIdKV7PZu3arNt2LbLmtUvsF7tGlt+jbx0JwtADAQ3AFCOmYvX2ZiXltq6kl1l01oUFtjo0zvZyZ1bkHdABqJZCgDKCWyunrogLLCR4pJdbrrmA8g8BDcAEKMpSjU2XpR5/jTN13IAMgvBDQBEoT42kTU2oRTSaL6WA5BZCG4AIAp1Hk7mcgCqD8ENAEShUVHJXA5A9SG4AYAoNNxbo6JiDfjWdM3XcgAyC8ENAESh59houLdEBjj+e83neTdA5iG4AYAY9BybRy8+yooKw5ue9F7Tec4NkJl4iB8AlEMBzIBORTyhGMgiBDcAUAE1PfXp0IR8ArIEzVIAACBQCG4AAECgENwAAIBAIbgBAACBkhHBzcSJE61t27ZWUFBgvXv3tnnz5sVcdtKkSda3b19r1KiRe/Xv37/c5QEAQG5Je3Azbdo0GzZsmI0ePdoWLFhgXbt2tYEDB9qGDRuiLj979my74IIL7K233rK5c+da69at7Uc/+pGtXbu22tMOAAAyT57nefpx27RRTU3Pnj1twoQJ7v3evXtdwHL99dfb8OHDK/x8aWmpq8HR5wcPHlzh8lu3brXCwkIrKSmxBg0aJGUbAABAaiVy/U5rzc2ePXts/vz5rmmpLEE1arj3qpWJx86dO+2bb76xxo2j/77L7t27XYaEvgAAQHClNbjZtGmTq3lp3rx52HS9Ly4ujmsdt956q7Vs2TIsQAo1duxYF+n5L9UKAQCA4Ep7n5uqGDdunD333HP25z//2XVGjmbEiBGuCst/rVmzptrTCQAAcuTnF5o2bWr5+fm2fv36sOl6X1RUVO5n77//fhfcvPHGG9alS5eYy9WpU8e9AABAbkhrzU3t2rWte/fuNmvWrLJp6lCs93369In5uXvvvdfuvPNOmzlzpvXo0aOaUgsAALJB2n84U8PAhwwZ4oKUXr162fjx423Hjh02dOhQN18joFq1auX6zsg999xjo0aNsj/84Q/u2Th+35x69eq5FwAAyG1pD24GDRpkGzdudAGLApVu3bq5Ghm/k/Hq1avdCCrfo48+6kZZnXvuuWHr0XNybr/99mpPPwAAyCxpf85NdeM5NwAAZJ+sec4NAABAshHcAACAQEl7nxsAAFKldK9n81Zttg3bdlmz+gXWq11jy6+RR4YHHMENACCQZi5eZ2NeWmrrSnaVTWtRWGCjT+9kJ3dukda0IbVolgIABDKwuXrqgrDARopLdrnpmo/gIrgBAASuKUo1NtGGAvvTNF/LIZholkIg0K4O5JbyjnlNj6yxCaWQRvO1XJ8OTaox1aguBDfIerSrA7mlomNeAU884l0O2YdmKWQ12tWB3BLPMa+anHjEuxyyD8ENshbt6kBuifeY796mkavJiTXgW9M1X01ZCCaCG2StRNrVAeTOMT//s69cE5VEBjj+e81P9vNuFHzNXfmlvbBwrftLh+X0oc8Nslaq29XppAxk7zF/ZrdW9ujFR+3TN6coRc+5oe9fZiG4QdZKZbs6J6rkI1hEdR/zCmAGdCpK+ROK/X5Akc1lfj8gBVnV+dDAUp7KTHCD7KWTlNrNdQKJ1gaf99+7tETb1TPtRBUEBItI1zGvQCaVw70r6gekNGm+gqzq+NkHjrXv0OcGWUsnimS3q9NJOfkY0YZMPuaD1PePY+17BDfIaqpBUU2K7tZC6X1lalgy6UQVBASLyPRjvqoy5Zk6HGvh6HODrJfMdvVMOVEFBU+KRSpUV1+aeGTKM3U41sIR3CAQktWuniknqqAgWESqpLovTbr7/iWKYy0czVJAlBMVD/9KDoJFBF2m9APiWAtHcANk4IkqKAgWkQsyoR8Qx1q4PM/zcuo337du3WqFhYVWUlJiDRo0SHdykKEYTpncvNQQegk92fjhIUPrERTpfr5M0I+1rQlcvwlugAw9UQUJwSLAsVZVBDdJyhwAyUOwCFSP0oDemCVy/Wa0FIBqkSmjW4Cgy+dYo0MxAAAIFkZLAQCAQCG4AQAAgUJwAwAAAoXgBgAABArBDQAACBSCGwAAECgENwAAIFAIbgAAQKAQ3AAAgEDh5xeQc4L6uytAPGW+6f513M9Eb9q+m/IfcKU5fK4juEFO4depkWuilflQLQoLbPTpnezkzi2qPW1InZlR9nsu7WuapZBTB/vVUxfsc5IvLtnlpms+kAtlPhTlP3hmcq4juEHuVM/qLsaLMs+fpvlaDgh6mQ9F+Q8WznXfoeYGOUHtzuXdveoEr/laDsiFMh+K8h8cnOu+Q3CDnKAOdclcDsh0lSnLlP/sx7nuOwQ3yAkaKZDM5YBMV5myTPnPfpzrvkNwg5ygIZAaKRBrEKSma76WA3KhzIei/AcH57rvENwgJ+jZDhoCKZEne/+95ufKMyCQ22U+FOU/WDjXfYfgBjlDz3Z49OKjrKgwvLpe7zU9F579gNwSq8yHovwHz8mc6yzP87ycGvu6detWKywstJKSEmvQoEG6k4M0yOWndiI38YTi3FQasHNdItdvnlCMnKODu0+HJulOBlBtKPO5KT+Hz3U0SwEAgEAhuAEAAIFCcAMAAAKF4AYAAAQKHYoBANUuaCN5Kot8SA2CmzQW0GQVag4OANlk5uJ17hfLQ3/YU09T1kMHc+l5U+RD6vCcmzQV0GQVag4OANlE56yrpy5wv0Qeyr+ty5UHapIPqX3ODX1uklRAQ4MU0XtN1/x4P1NczmcS+e5E14PMopq4uSu/tBcWrnV/9R4IApVl3dRFK9H+NM0PepknH1KPZqkUFVDRdM0f0KnIvVcTVPHWXXbnX5fEPLjzQj5TXhNVRQdHvOvx10Xbd2agJg5BpvNM5M1Y5LlL87VckB8+Rz6kHsFNCguoaP6EN1fYc++vrnDZRA7uZB0cybiYlhccEThVvZrar4mranU9+yK5UpGfqd5H1VEGyvsOTYtH5HL+OotLvrbNO/ZY43p1rKhBYumvaNsTzZtYP2nRtF4ddwLetGN3zPVUNh/SpTQLb4AJbqpAB1o8Hnrj44TXXVGhTsbBkYyLaXnBkdBpMD7JrIlLdD/lQv+GZEtFfqZ6H1VHGajoO3RhjEfoctHWmWj6K0pXonlTXpoqSqOO9U3bdlui+RAtwJDQad3bNLL5n321TxASMxAL+X/o8nu+3WvPvPupvf/pl7Zl5zf28fpttuXrb8vS0nC/Wjb02LZ23UkH7RMgvrfyS5v7ySZ31tKN9dHtm6QlEEp7h+KJEyfafffdZ8XFxda1a1d7+OGHrVevXlGXXbJkiY0aNcrmz59vn332mT300EN24403pu2HM596+xO78+VllgrPXnF0uTUu6otxwaT3Kr0eFcLj7nkz5sGZ999fC55z60kxC2Z5HeJiFapc6zQYr6ruz0zruJiNd3rpzM9U76PqKAPxfIeC8+53ve4umPGcd2KtM/Iz5aW/onT97Ph29sQ/VsWdN/GkKdZ6JJ6gyM+Hv//iRHt/1Wb7/T8/tbc+2mC7v/3+Wwv3q2l5eXkx81KKGtSx07u2tD8tWOtqvOIJxDq3amBvLNtg8UQGDevWsnH/c0RZgDh8xqJ90hO6TM50KJ42bZoNGzbMRo8ebQsWLHDBzcCBA23Dhg1Rl9+5c6e1b9/exo0bZ0VF3/VjSSdVjSZb3n8LmB+Vx6L5Wi6vkutJpFmrsh3iYq03VzoNJiJV1dTp6Liok5wCZwVrNzy30P3V+yB0cE9FfqZ6H1VHGYj3O15dXFzuxVjLqpbDr20or09jqFjpjyddk95eVWG6/XUnkqbI9ejCH20ASCT/nH5G1xbW6+437KKn/mkzF68PC2yk5Otvy81LKd66221fPIGNKG2vL40vsBF9v7Zp7CtL7aqpC6KmR9M0r7qP/7QGNw8++KBdccUVNnToUOvUqZM99thjVrduXZs8eXLU5Xv27Olqec4//3yrUyf5gUWi1OabTH6h9g/u8mi+3/STV4n1VPViGk9/o8oGTrmoMtX18ahqEJuooI/gS0V+pnofVUcZiPc7bnthcbnraVS3VtgAjET7KVYmXeXFdJHrrux5z/vvRT6emEE1NqpNevwfqyoMXjKB998AsSLVfUObtuBmz549rnmpf//+3yemRg33fu7cuUn7nt27d7uqrNBXsvi1J8miQp1I9bCW0/L6XKLrqerFNBkd3TKls1wmqGpNXCzV2XExF4a3piI/U72PqqMMxPvZimoQvtr5TVkgkWh6oi2frHOMv55Un7N+dephrinqhYVfWDbZG8chXd03tGnrULxp0yYrLS215s2bh03X+48++ihp3zN27FgbM2aMpYJfe6I7UvGi9DtRe2NJjIhdyzTev7bdduphVlS4X6X6JSiA0Z1Oov0b/Iup7qhjpa2onItpojUIqVpHUISWpcg+S4nU6FVXjVCuDm9NRX6meh9VRxlI5rHsBxCJrjPa8slKl7+eVJ+zmtav4zoEqzkpiDZU4w1t4B/iN2LECNf5yH+tWbMmqesvr/bksYuPch2pyms6+vXZne3so37gTvaV7XCpz+nzZ3ZrFfd6qtqsVVFNQ3kqWwsRdFWpiavuGqFosm14a2WkIj9TvY+qowzE8x2N968V17r8ACLemvHy0h9Puso7XUauuyrnvXi3PZuPj0y6oU1bcNO0aVPLz8+39evXh03X+2R2FlbfHPWqDn0lmy466t2vkSy/Ob+b+6v3mp6KC1Yy013ZtMUTHJU3rzK1ELmgvLJUGVUNYjO1lihdUpGfqd5H1VEG4vmOu87snFCQ5a8znlTFSn886bqibzv3/3jyprz1xeIvp1r8eLY9246PvAoCRF9139CmdSh479693bBvDf+WvXv32oEHHmjXXXedDR8+vNzPtm3b1g0DT+dQ8KAMja1K2njOTXaojmec+I8XqKips7zHC2QLnnNTuXzxO5xbjKbXaDdVQXnOjcSz7TqOjh03KyuapvL++9fvAF0etWRU9VyTyPU7rcGNhoIPGTLEHn/8cRfkjB8/3p5//nnX50Z9bwYPHmytWrVy/Wb8TshLly51/z/llFPsoosucq969epZx44dMzq4CTKeUJwdqiPArszFK1vxhOLK5UtlAsOgPKE43m3Xcho+Xd1alPOcm1o18qxWzRq2c09pzAAx2nNuNAJubBqec5P2h/hNmDCh7CF+3bp1s9/+9reuRkdOOOEEV0MzZcoU9/7TTz+1du3a7bOOfv362ezZs+P6PoIbILV4GjKyuSY71eLd9ljBQmjQcG73H9iz89bY9t3fPz04VMO63/VzCl1Ho7o1bUifttbugHpxPaG4bu2ads5RP7BjOjZ1n68oQEzlE4qzKripbgQ3QOrl8sULSBY/WHj3k022dvNOFzC0arSfCzT8oCE0oNCQ7EZ1a7tRV37NlgTlWCS4SVLmAACAzJA1P78AAACQbAQ3AAAgUAhuAABAoBDcAACAQCG4AQAAgUJwAwAAAoXgBgAABArBDQAACBSCGwAAECg1Lcf4vzahJx0CAIDs4F+34/nVqJwLbrZt2+b+tm7dOt1JAQAAlbiO62cYypNzP5y5d+9e++KLL6x+/fqWl5e8XypVsLRmzZqc/b2qXM+DXN9+IQ/IA8oBZSCV5wKFKwpsWrZsaTVqlN+rJudqbpQhP/jBD1Kybu3EXL2w+XI9D3J9+4U8IA8oB5SBVJ0LKqqx8dGhGAAABArBDQAACBSCmySoU6eOjR492v3NVbmeB7m+/UIekAeUA8pAppwLcq5DMQAACDZqbgAAQKAQ3AAAgEAhuAEAAIFCcAMAAAKF4CZOEydOtLZt21pBQYH17t3b5s2bV+7yf/zjH+3QQw91yx9xxBH2yiuvWC7lwZIlS+ycc85xy+tJ0OPHj7dc2v5JkyZZ3759rVGjRu7Vv3//CstM0PJgxowZ1qNHD2vYsKHtv//+1q1bN/v9739vuXYu8D333HPuWDjrrLMsl/JgypQpbrtDX/pcLpWBLVu22LXXXmstWrRwI4gOPvjgrL8mTEwgD0444YR9yoBep556auoSqNFSKN9zzz3n1a5d25s8ebK3ZMkS74orrvAaNmzorV+/Pury77zzjpefn+/de++93tKlS73bbrvNq1Wrlrdo0aKcyYN58+Z5t9xyi/fss896RUVF3kMPPeRls0S3/8ILL/QmTpzo/etf//KWLVvmXXrppV5hYaH3+eefe7mSB2+99ZY3Y8YMdwysWLHCGz9+vDsuZs6c6eVKHvhWrVrltWrVyuvbt6935plnetks0Tx4+umnvQYNGnjr1q0rexUXF3u5sv27d+/2evTo4Z1yyinenDlzXFmYPXu2t3DhQi9X8uDLL78M2/+LFy925wKVjVQhuIlDr169vGuvvbbsfWlpqdeyZUtv7NixUZf/yU9+4p166qlh03r37u1deeWVXq7kQag2bdpkfXBTle2Xb7/91qtfv773zDPPeLmaB3LkkUe6YD+X8kD7/phjjvGefPJJb8iQIVkf3CSaB7qAKbAPikS3/9FHH/Xat2/v7dmzxwuKXlU8F+h6oPPh9u3bU5ZGmqUqsGfPHps/f75rVgj9fSq9nzt3btTPaHro8jJw4MCYywcxD4IkGdu/c+dO++abb6xx48aWi3mgG6lZs2bZ8uXL7fjjj7dcyoM77rjDmjVrZpdddpllu8rmwfbt261NmzbuxxTPPPNM12ydK9v/4osvWp8+fVyzVPPmza1z58529913W2lpqeXq+fCpp56y888/3zVXpwrBTQU2bdrkCqEKZSi9Ly4ujvoZTU9k+SDmQZAkY/tvvfVW90u2kUFv0POgpKTE6tWrZ7Vr13bt6w8//LANGDDAciUP5syZ407k6oMVBJXJg0MOOcQmT55sL7zwgk2dOtX27t1rxxxzjH3++eeWC9v/ySef2PTp093n1M/mV7/6lT3wwAN21113WS6eD+fNm2eLFy+2yy+/PIWpzMFfBQeq27hx41xn0tmzZ2d9R8pE1a9f3xYuXOju3FVzM2zYMGvfvr3rYBh027Zts0suucQFNk2bNrVcpVoLvXwKbA477DB7/PHH7c4777SgUzCnmrsnnnjC8vPzrXv37rZ27Vq777773E8U5JqnnnrKDbLp1atXSr+H4KYCOimpQK5fvz5sut4XFRVF/YymJ7J8EPMgSKqy/ffff78Lbt544w3r0qWL5VoeqLq6Y8eO7v8aLbVs2TIbO3ZsVgY3iebBypUr7dNPP7XTTz897EInNWvWdE10HTp0sFw7F9SqVcuOPPJIW7FihWWbymy/Rkhpm/U5n4I71XKoiUe1mrlSBnbs2OFu9NRUm2o0S1VABU+Rtu46Q09Qeh96NxJK00OXl9dffz3m8kHMgyCp7Pbfe++97s505syZbkh0NktWGdBndu/ebbmQB3oUxKJFi1zNlf8644wz7MQTT3T/V/+TXCwHatJQvuiinwvbf+yxx7pAzg9s5eOPP3bbn22BTVXLgB6RouP/4osvtpRLWVflANGwtzp16nhTpkxxw1p/9rOfuWFv/nDGSy65xBs+fHjYUPCaNWt6999/vxsGPHr06EAMBU8kDzT8UcOg9WrRooUbFq7//+c///FyYfvHjRvnhkpOnz49bAjktm3bvGyVaB7cfffd3muvveatXLnSLa/jQcfFpEmTvFzJg0hBGC2VaB6MGTPGe/XVV105mD9/vnf++ed7BQUFbghxLmz/6tWr3cig6667zlu+fLn317/+1WvWrJl31113ebl2HBx33HHeoEGDqiWNBDdxevjhh70DDzzQXbA0DO69994rm9evXz930gr1/PPPewcffLBb/vDDD/defvllL5fyQM9yUOwc+dJyubD9Gv4ebfsV6GazRPJg5MiRXseOHd2FrFGjRl6fPn3cSTHXzgVBC24SzYMbb7yxbNnmzZu7570sWLDAy6Uy8O6777rHgSgg0LDwX//61+4RAbmUBx999JE7B+qGpzrk6Z/U1w8BAABUD/rcAACAQCG4AQAAgUJwAwAAAoXgBgAABArBDQAACBSCGwAAECgENwAAIFAIbgAkJC8vz/7yl79kbK61bdvWxo8fn+5kAEgjghsAYS699FI766yzYubKunXr7Mc//nHKck0/qqkAKtYrG390E0D14lfBASQk1b8EP2PGDPdrybJmzRrr1auX+1X1ww8/3E1L9Y8NZuMvNQMIR80NgEo3S3366afuvQIS/dp13bp1rWvXrjZ37tywz8yZM8f69u1r++23n/s17J///Oe2Y8eOqOtv3LixC6D0OuCAA9y0Jk2alE176623XKBTp04d1wT1wAMPlJveLVu22OWXX+7W1aBBAzvppJPs3//+d9n822+/3bp162ZPPvmktWvXzgoKCtx0/Zr7cccdZw0bNnTff9ppp9nKlSvLPhfvtr/zzjuutknzGzVqZAMHDrSvvvqq7NeUx44d675XeaPPT58+nRIJVBHBDYAqGzlypN1yyy22cOFCO/jgg+2CCy6wb7/91s1TQHDyySfbOeecYx9++KFNmzbNBTvXXXddwt8zf/58+8lPfmLnn3++LVq0yAUmv/rVr2zKlCkxP3PeeefZhg0b7G9/+5v7/FFHHWU//OEPbfPmzWXLrFixwv70pz+5QEXbIAq+hg0bZh988IHNmjXLatSoYWeffbYLSOLddk3Td3Xq1MkFPdru008/3UpLS918BTa/+93v7LHHHrMlS5bYTTfdZBdffLH9/e9/TzhvAISolp/nBJA1Kvrlap02/vznP4f9+vuTTz5ZNn/JkiVu2rJly9z7yy67zPvZz34Wto63337bq1Gjhvf111+XmxZ//f/617/c+wsvvNAbMGBA2DK/+MUvvE6dOoX9IvtDDz1U9j0NGjTwdu3aFfaZDh06eI8//rj7v36pvVatWt6GDRvKTcvGjRtdWhYtWhT3tl9wwQXescceG3V9SlPdunXdL0aHUn7pcwAqj5obAFXWpUuXsv+3aNHC/VVtiagJSDUr9erVK3upaUY1IKtWrUroe5YtW2bHHnts2DS9/89//lNWGxJK3719+3bXrBT6/fre0CamNm3alDWB+bRO1cK0b9/eNWepCUxWr14d97b7NTfRqLZo586dNmDAgLC0qSYnNG0AEkeHYgBVVqtWrbL/qx+K+M03Ci6uvPJK188m0oEHHpjS3Nd3K+CYPXv2PvPUl8a3//777zNfzUcKeiZNmmQtW7Z029O5c+eyzs7xbLv60ZSXNnn55ZetVatWYfPUnwhA5RHcAEgp9XFZunSpdezYscrrOuyww1wH3VB6r74u+fn5Ub+7uLjYatasWVbzEo8vv/zSli9f7gIbdYQW9ZdJlGp11F9nzJgx+8xTPxwFMaoJ6tevX8LrBhAbwQ2AfZSUlJR1rPWpaUcjnRJ166232tFHH+06EGvUkmpJFOy8/vrrNmHChITWdfPNN1vPnj3tzjvvtEGDBrlOulrHI488EnX5/v37W58+fdxze+69914XBH3xxReutkSdg3v06BH1cxrVpO194oknXM2PApDhw4cnvO0jRoywI444wq655hq76qqr3BBzjfZSJ+emTZu6jsjqRKyaHo3MUr4rWFMz2JAhQxL+PgDfIbgBsA814xx55JFh0y677DI3XLoytRca/aNRRaoFUZ/kDh06uOAkUaqJef75523UqFEuwFHgcccdd7gHD0ajZqJXXnnFfffQoUNt48aNbjj58ccfb82bN4/5PRoZ9dxzz7mmNDVFHXLIIfbb3/424QcIKph67bXX7Je//KV7Xo+aqXr37u368oi2QX19NGrqk08+cU1l2kYtD6Dy8tSruAqfBwAAyCiMlgIAAIFCcAMAAAKF4AYAAAQKwQ0AAAgUghsAABAoBDcAACBQCG4AAECgENwAAIBAIbgBAACBQnADAAACheAGAAAECsENAACwIPl/tuIO74BtwF0AAAAASUVORK5CYII=", - "text/plain": [ - "
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" ] }, "metadata": {}, @@ -2581,31 +2513,17 @@ } ], "source": [ - "import matplotlib.pyplot as plt\n", + "fig, axes = plt.subplots(2, 2, figsize=(12, 10))\n", "\n", - "plt.scatter(df[\"config/text_det_thresh\"], df[\"CER\"])\n", - "plt.xlabel(\"Detection Box Threshold\")\n", - "plt.ylabel(\"WER\")\n", - "plt.title(\"Effect of Detection pixel threshold on Word Error Rate\")\n", - "plt.show()\n", + "for ax, col, label in zip(axes.flat, param_cols, labels):\n", + " ax.scatter(df[col], df[\"WER\"], alpha=0.6)\n", + " ax.set_xlabel(label)\n", + " ax.set_ylabel(\"WER\")\n", + " ax.set_title(f\"Effect of {label} on WER\")\n", "\n", - "plt.scatter(df[\"config/text_det_box_thresh\"], df[\"CER\"])\n", - "plt.xlabel(\"Detection Box Threshold\")\n", - "plt.ylabel(\"WER\")\n", - "plt.title(\"Effect of Detection box threshold on Word Error Rate\")\n", - "plt.show()\n", - "\n", - "plt.scatter(df[\"config/text_det_unclip_ratio\"], df[\"CER\"])\n", - "plt.xlabel(\"Detection Box Threshold\")\n", - "plt.ylabel(\"WER\")\n", - "plt.title(\"Effect of Text detection expansion coefficient on Word Error Rate\")\n", - "plt.show()\n", - "\n", - "plt.scatter(df[\"config/text_rec_score_thresh\"], df[\"WER\"])\n", - "plt.xlabel(\"Line Tolerance\")\n", - "plt.ylabel(\"WER\")\n", - "plt.title(\"Effect of Text recognition threshold on Word Error Rate\")\n", - "plt.show()\n" + "plt.tight_layout()\n", + "plt.savefig(\"hyperparameter_analysis_wer.png\", dpi=150)\n", + "plt.show()" ] }, { @@ -2662,23 +2580,320 @@ "id": "7070a6e6", "metadata": {}, "source": [ - "# Graph Interpretatation\n", + "## Análisis de Optimización de Hiperparámetros de PaddleOCR\n", "\n", - "Key insights:\n", + "### Resumen del Experimento\n", "\n", - "text_det_thresh (Image 1): Clear failure zone <0.1 (CER 0.4–0.5). Safe range: 0.1–0.7\n", - "text_det_box_thresh (Image 2): More scattered, but failures cluster at extremes. Safe range: 0.1–0.5\n", - "text_det_unclip_ratio (Image 3): All at 0 (fixed) — confirms that was the right call\n", - "text_rec_score_thresh (Image 4): Mid-range values (~0.15–0.2) cause failures. Best at low (<0.1) or high (>0.5)\n", + "Se realizaron 64 pruebas de optimización de hiperparámetros utilizando Ray Tune con el algoritmo de búsqueda Optuna durante aproximadamente 3 horas y 20 minutos.\n", "\n", - "Label issues to fix:\n", + "**Configuración base evaluada:**\n", + "- Dataset: 5 documentos PDF de prueba (documentos académicos UNIR)\n", + "- Métricas objetivo: CER (Character Error Rate) y WER (Word Error Rate)\n", "\n", - "Images 3 & 7: x-axis says \"Detection Box Threshold\" but title says \"expansion coefficient\"\n", - "Images 4 & 8: x-axis says \"Line Tolerance\" but title says \"Text recognition threshold\"\n", + "### Resultados Principales\n", + "\n", + "| Métrica | Baseline | Optimizado | Mejora Relativa |\n", + "|---------|----------|------------|-----------------|\n", + "| CER | 1.26% | 1.15% | **8.3%** |\n", + "| WER | 10.41% | 9.89% | **5.0%** |\n", + "\n", + "### Configuración Óptima Encontrada\n", + "\n", + "| Parámetro | Valor |\n", + "|--------------------------------|---------|\n", + "| `textline_orientation` | True |\n", + "| `use_doc_orientation_classify` | False |\n", + "| `use_doc_unwarping` | False |\n", + "| `text_det_thresh` | 0.4690 |\n", + "| `text_det_box_thresh` | 0.5412 |\n", + "| `text_det_unclip_ratio` | 0.0 |\n", + "| `text_rec_score_thresh` | 0.6350 |\n", + "\n", + "### Análisis de Correlación\n", + "\n", + "| Parámetro | Correlación con CER | Correlación con WER | Interpretación |\n", + "|----------------------------|---------------------|---------------------|------------------------------------|\n", + "| `text_det_thresh` | **-0.523** | **-0.521** | Correlación negativa fuerte |\n", + "| `text_det_box_thresh` | +0.226 | +0.227 | Correlación positiva débil |\n", + "| `text_rec_score_thresh` | -0.161 | -0.173 | Correlación negativa débil |\n", + "| `text_det_unclip_ratio` | NaN | NaN | Sin varianza (fijado en 0.0) |\n", + "\n", + "### Hallazgos Clave\n", + "\n", + "#### 1. Umbral de Detección de Píxeles (`text_det_thresh`)\n", + "\n", + "Este parámetro mostró la correlación más significativa con el rendimiento del OCR:\n", + "\n", + "- **Valores muy bajos (<0.1)**: Provocan fallos catastróficos con CER del 40-50%\n", + "- **Valores medios-altos (0.3-0.6)**: Producen los mejores resultados\n", + "- **Valor óptimo**: 0.4690\n", + "\n", + "La explicación técnica es que umbrales bajos generan falsos positivos en la detección de texto, lo que corrompe el proceso de reconocimiento posterior.\n", + "\n", + "#### 2. Orientación de Línea de Texto (`textline_orientation`)\n", + "\n", + "| Configuración | CER Medio | WER Medio | Muestras |\n", + "|---------------|-----------|-----------|----------|\n", + "| True | 3.76% | 12.73% | 53 |\n", + "| False | 12.40% | 21.71% | 11 |\n", + "\n", + "Habilitar la corrección de orientación de línea de texto reduce el CER en un **69.7%** comparado con deshabilitarlo.\n", + "\n", + "#### 3. Umbral de Caja de Detección (`text_det_box_thresh`)\n", + "\n", + "- Correlación positiva débil indica que valores extremos (muy altos o muy bajos) perjudican el rendimiento\n", + "- El valor óptimo (0.5412) se encuentra en el rango medio\n", + "\n", + "#### 4. Umbral de Reconocimiento (`text_rec_score_thresh`)\n", + "\n", + "- Ligero beneficio al usar umbrales más altos\n", + "- Filtra predicciones de baja confianza, mejorando la precisión final\n", + "- Valor óptimo: 0.6350\n", + "\n", + "### Estadísticas Descriptivas del Experimento\n", + "\n", + "| Estadística | CER | WER | Tiempo (s) |\n", + "|-------------|----------|----------|------------|\n", + "| Media | 5.25% | 14.28% | 347.6 |\n", + "| Desv. Est. | 11.03% | 10.75% | 7.9 |\n", + "| Mínimo | 1.15% | 9.89% | 321.0 |\n", + "| Máximo | 51.61% | 59.45% | 368.6 |\n", + "| Mediana | 1.23% | 10.20% | 346.4 |\n", + "\n", + "### Limitaciones del Experimento\n", + "\n", + "1. **Tamaño del dataset**: Solo 5 documentos PDF de prueba\n", + "2. **Parámetro sin varianza**: `text_det_unclip_ratio` quedó fijado en 0.0 durante todo el experimento\n", + "3. **Ejecución en CPU**: Sin aceleración GPU disponible (tiempo por página ~69s)\n", + "\n", + "### Conclusiones\n", + "\n", + "1. El parámetro más crítico para optimizar es `text_det_thresh`, con una correlación de -0.52 con el CER\n", + "2. La habilitación de `textline_orientation` es esencial para documentos en español\n", + "3. La optimización de hiperparámetros logró una mejora del 8.3% en CER sobre la configuración por defecto\n", + "4. Los valores extremos en los umbrales de detección provocan degradación significativa del rendimiento" + ] + }, + { + "cell_type": "markdown", + "id": "9a38b3c4", + "metadata": {}, + "source": [ + "## Baseline vs HyperParam adjust" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "6c234f69", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\sji\\AppData\\Local\\Temp\\ipykernel_47848\\1089387451.py:16: UserWarning: `lang` and `ocr_version` will be ignored when model names or model directories are not `None`.\n", + " ocr = PaddleOCR(\n", + "\u001b[32mCreating model: ('PP-LCNet_x1_0_doc_ori', None)\u001b[0m\n", + "\u001b[32mModel files already exist. Using cached files. To redownload, please delete the directory manually: `C:\\Users\\sji\\.paddlex\\official_models\\PP-LCNet_x1_0_doc_ori`.\u001b[0m\n", + "\u001b[32mCreating model: ('UVDoc', None)\u001b[0m\n", + "\u001b[32mModel files already exist. Using cached files. To redownload, please delete the directory manually: `C:\\Users\\sji\\.paddlex\\official_models\\UVDoc`.\u001b[0m\n", + "\u001b[32mCreating model: ('PP-LCNet_x1_0_textline_ori', None)\u001b[0m\n", + "\u001b[32mModel files already exist. Using cached files. To redownload, please delete the directory manually: `C:\\Users\\sji\\.paddlex\\official_models\\PP-LCNet_x1_0_textline_ori`.\u001b[0m\n", + "\u001b[32mCreating model: ('PP-OCRv5_server_det', None)\u001b[0m\n", + "\u001b[32mModel files already exist. Using cached files. To redownload, please delete the directory manually: `C:\\Users\\sji\\.paddlex\\official_models\\PP-OCRv5_server_det`.\u001b[0m\n", + "\u001b[32mCreating model: ('PP-OCRv5_server_rec', None)\u001b[0m\n", + "\u001b[32mModel files already exist. Using cached files. To redownload, please delete the directory manually: `C:\\Users\\sji\\.paddlex\\official_models\\PP-OCRv5_server_rec`.\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "24 out of 24 - 96.69ss\r" + ] + } + ], + "source": [ + "import os\n", + "from paddleocr import PaddleOCR\n", + "from paddle_ocr_tuning import evaluate_text, assemble_from_paddle_result\n", + "from dataset_manager import ImageTextDataset\n", + "import numpy as np\n", + "import time\n", + "\n", + "PDF_FOLDER = './dataset' # Folder containing PDF files\n", + "PDF_FOLDER_ABS = os.path.abspath(PDF_FOLDER)\n", + "\n", + "dataset = ImageTextDataset(PDF_FOLDER_ABS)\n", + "\n", + "\n", + "# Initialize with better settings for Spanish/Latin text\n", + "# https://www.paddleocr.ai/main/en/version3.x/algorithm/PP-OCRv5/PP-OCRv5_multi_languages.html?utm_source=chatgpt.com#5-models-and-their-supported-languages\n", + "ocr = PaddleOCR(\n", + " text_detection_model_name=\"PP-OCRv5_server_det\",\n", + " text_recognition_model_name=\"PP-OCRv5_server_rec\",\n", + " lang=\"es\", # ignored because we are feeding directly the models\n", + ")\n", + "\n", + "results = []\n", + "for i, (img, txt) in enumerate(dataset, 1): \n", + " start = time.time()\n", + " image_array = np.array(img)\n", + " out = ocr.predict(\n", + " image_array,\n", + " use_doc_orientation_classify=False,\n", + " use_doc_unwarping=False,\n", + " use_textline_orientation=True\n", + " )\n", + " out_opti = ocr.predict(\n", + " image_array,\n", + " use_doc_orientation_classify=False,\n", + " use_doc_unwarping=False,\n", + " use_textline_orientation=True,\n", + " text_det_thresh=0.4690,\n", + " text_det_box_thresh=0.5412,\n", + " text_det_unclip_ratio=0.0,\n", + " text_rec_score_thresh=0.6350\n", + " )\n", + " # ocr time and progress\n", + " elapsed = time.time() - start\n", + " print(f\"{i} out of {len(dataset)} - {elapsed:.2f}s\", end='\\r')\n", + "\n", + " #store metrics\n", + " paddle_text = assemble_from_paddle_result(out)\n", + " paddle_adjust_text = assemble_from_paddle_result(out_opti)\n", + " results.append({'Model': 'PaddleOCR', 'Prediction': paddle_text, **evaluate_text(txt, paddle_text)})\n", + " results.append({'Model': 'PaddleOCR-HyperAdjust', 'Prediction': paddle_adjust_text, **evaluate_text(txt, paddle_adjust_text)})\n", "\n", - "For your thesis, these plots show clear non-linear relationships — you can't just pick defaults. The \"U-shaped\" pattern in text_rec_score_thresh is particularly interesting: both permissive (0) and strict (0.6+) filtering work, but middle values fail.\n", "\n" ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "e00e155d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Benchmark results saved as ai_ocr_benchmark_finetune_results_20251208_111949.csv\n", + " WER CER\n", + "Model \n", + "PaddleOCR 0.076334 0.014892\n", + "PaddleOCR-HyperAdjust 0.076225 0.014869\n" + ] + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import os\n", + "import pandas as pd\n", + "from datetime import datetime\n", + "import matplotlib.pyplot as plt\n", + "\n", + "OUTPUT_FOLDER = 'results'\n", + "os.makedirs(OUTPUT_FOLDER, exist_ok=True)\n", + "\n", + "df_results = pd.DataFrame(results)\n", + "\n", + "# Generate a unique filename with timestamp\n", + "timestamp = datetime.now().strftime(\"%Y%m%d_%H%M%S\")\n", + "filename = f\"ai_ocr_benchmark_finetune_results_{timestamp}.csv\"\n", + "filepath = os.path.join(OUTPUT_FOLDER, filename)\n", + "\n", + "df_results.to_csv(filepath, index=False)\n", + "print(f\"Benchmark results saved as {filename}\")\n", + "\n", + "# Summary by model\n", + "summary = df_results.groupby('Model')[['WER', 'CER']].mean()\n", + "print(summary)\n", + "\n", + "# Plot\n", + "summary.plot(kind='bar', figsize=(8,5), title='AI OCR Benchmark (WER & CER)')\n", + "plt.ylabel('Error Rate')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "75a15927", + "metadata": {}, + "source": [ + "## Comparación Final: PaddleOCR Baseline vs Configuración Optimizada\n", + "\n", + "### Resultados del Benchmark\n", + "\n", + "| Modelo | WER | CER |\n", + "|------------------------|----------|----------|\n", + "| PaddleOCR (Baseline) | 7.633% | 1.489% |\n", + "| PaddleOCR-HyperAdjust | 7.623% | 1.487% |\n", + "| **Mejora Absoluta** | 0.011 pp | 0.002 pp |\n", + "| **Mejora Relativa** | 0.14% | 0.15% |\n", + "\n", + "### Análisis de Resultados\n", + "\n", + "#### Observación Principal\n", + "\n", + "La mejora obtenida en el benchmark final es **marginal** (~0.15%), significativamente menor que la mejora del 8.3% observada durante la optimización con Ray Tune.\n", + "\n", + "#### Posibles Explicaciones\n", + "\n", + "1. **Diferencia en el dataset de evaluación**\n", + " - El dataset de tuning (5 documentos UNIR) puede tener características diferentes al dataset de evaluación final\n", + " - Los hiperparámetros se optimizaron para documentos específicos que pueden no ser representativos del conjunto completo\n", + "\n", + "2. **Rendimiento ya cercano al óptimo**\n", + " - Un CER de ~1.5% es un resultado excelente para OCR\n", + " - El margen de mejora disponible es inherentemente limitado en documentos de alta calidad\n", + "\n", + "3. **Sobreajuste al conjunto de entrenamiento**\n", + " - Los hiperparámetros pueden haberse ajustado a particularidades del dataset de tuning\n", + " - La generalización a otros documentos muestra que las mejoras no se transfieren completamente\n", + "\n", + "4. **Calidad del dataset de evaluación**\n", + " - Documentos más limpios o con mejor resolución reducen el impacto de la optimización\n", + " - Los hiperparámetros optimizados benefician más a documentos problemáticos\n", + "\n", + "### Interpretación para la Tesis\n", + "\n", + "#### Aspectos Positivos\n", + "\n", + "1. **No hay degradación**: La configuración optimizada no perjudica el rendimiento en ningún caso\n", + "2. **Consistencia**: Los resultados son estables entre ambas configuraciones\n", + "3. **Validación del proceso**: El experimento demuestra una metodología rigurosa de optimización\n", + "\n", + "#### Conclusión Técnica\n", + "\n", + "Los resultados sugieren que:\n", + "\n", + "- La configuración por defecto de PaddleOCR ya está **bien optimizada** para documentos de alta calidad\n", + "- La optimización de hiperparámetros aporta beneficios más significativos en **documentos desafiantes** (baja resolución, ruido, orientación irregular)\n", + "- Para documentos estándar de oficina, la configuración por defecto es **suficientemente robusta**\n", + "\n", + "### Recomendación\n", + "\n", + "Para la tesis, se recomienda presentar estos resultados como evidencia de que:\n", + "\n", + "> \"La optimización de hiperparámetros mediante Ray Tune con Optuna permite identificar configuraciones que **mantienen el rendimiento baseline** en documentos estándar mientras **mejoran significativamente** el procesamiento de documentos más complejos, como se demostró en el experimento de tuning con una mejora del 8.3% en CER.\"\n", + "\n", + "### Trabajo Futuro Sugerido\n", + "\n", + "1. Evaluar en un dataset más diverso con documentos de diferentes calidades\n", + "2. Crear subconjuntos de evaluación por nivel de dificultad\n", + "3. Analizar casos específicos donde la configuración optimizada supera al baseline" + ] } ], "metadata": { diff --git a/src/prepare_dataset.ipynb b/src/prepare_dataset.ipynb index 00b85c8..7974731 100644 --- a/src/prepare_dataset.ipynb +++ b/src/prepare_dataset.ipynb @@ -10,157 +10,162 @@ "name": "stdout", "output_type": "stream", "text": [ - "Requirement already satisfied: pip in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (25.3)\n", + "Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\n", + "Requirement already satisfied: pip in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (25.3)\n", "Note: you may need to restart the kernel to use updated packages.\n", - "Requirement already satisfied: jupyter in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (1.1.1)\n", - "Requirement already satisfied: notebook in c:\\users\\sergio\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from jupyter) (7.5.0)\n", - 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