diff --git a/.gitea/workflows/ci.yaml b/.gitea/workflows/ci.yaml
index 37df5e8..26510e4 100644
--- a/.gitea/workflows/ci.yaml
+++ b/.gitea/workflows/ci.yaml
@@ -12,7 +12,6 @@ on:
env:
PADDLE_VERSION: "3.0.0"
- WHEEL_BASE_URL: "https://seryus.ddns.net/api/packages/unir/generic"
jobs:
essential:
@@ -33,22 +32,14 @@ jobs:
echo "Version: 1.0.${{ gitea.run_number }}" >> $GITHUB_STEP_SUMMARY
echo "Event: ${{ gitea.event_name }}" >> $GITHUB_STEP_SUMMARY
- # CPU image: Matrix build for amd64 and arm64
+ # PaddleOCR CPU image (amd64 only)
build_cpu:
runs-on: ubuntu-latest
needs: essential
- strategy:
- matrix:
- platform:
- - linux/amd64
- - linux/arm64
steps:
- name: Checkout
uses: actions/checkout@v4
- - name: Set up QEMU
- uses: docker/setup-qemu-action@v3
-
- name: Set up Docker Buildx
uses: docker/setup-buildx-action@v3
@@ -59,50 +50,25 @@ jobs:
username: username
password: ${{ secrets.CI_READWRITE }}
- - name: Get arch suffix
- id: arch
- run: |
- if [ "${{ matrix.platform }}" = "linux/amd64" ]; then
- echo "suffix=amd64" >> $GITHUB_OUTPUT
- else
- echo "suffix=arm64" >> $GITHUB_OUTPUT
- fi
-
- - name: Download ARM64 wheel from Gitea packages
- if: matrix.platform == 'linux/arm64'
- run: |
- mkdir -p src/paddle_ocr/wheels
- curl -L -o src/paddle_ocr/wheels/paddlepaddle-${{ env.PADDLE_VERSION }}-cp311-cp311-linux_aarch64.whl \
- "${{ env.WHEEL_BASE_URL }}/paddlepaddle-cpu-arm64/${{ env.PADDLE_VERSION }}/paddlepaddle-${{ env.PADDLE_VERSION }}-cp311-cp311-linux_aarch64.whl"
- ls -la src/paddle_ocr/wheels/
-
- - name: Build and push CPU image (${{ matrix.platform }})
+ - name: Build and push CPU image
uses: docker/build-push-action@v5
with:
context: src/paddle_ocr
file: src/paddle_ocr/Dockerfile.cpu
- platforms: ${{ matrix.platform }}
+ platforms: linux/amd64
push: true
tags: |
- ${{ needs.essential.outputs.image_cpu }}:${{ needs.essential.outputs.Version }}-${{ steps.arch.outputs.suffix }}
- ${{ needs.essential.outputs.image_cpu }}:${{ steps.arch.outputs.suffix }}
+ ${{ needs.essential.outputs.image_cpu }}:${{ needs.essential.outputs.Version }}
+ ${{ needs.essential.outputs.image_cpu }}:latest
- # GPU image: Matrix build for amd64 and arm64
+ # PaddleOCR GPU image (amd64 only)
build_gpu:
runs-on: ubuntu-latest
needs: essential
- strategy:
- matrix:
- platform:
- - linux/amd64
- - linux/arm64
steps:
- name: Checkout
uses: actions/checkout@v4
- - name: Set up QEMU
- uses: docker/setup-qemu-action@v3
-
- name: Set up Docker Buildx
uses: docker/setup-buildx-action@v3
@@ -113,92 +79,25 @@ jobs:
username: username
password: ${{ secrets.CI_READWRITE }}
- - name: Get arch suffix
- id: arch
- run: |
- if [ "${{ matrix.platform }}" = "linux/amd64" ]; then
- echo "suffix=amd64" >> $GITHUB_OUTPUT
- else
- echo "suffix=arm64" >> $GITHUB_OUTPUT
- fi
-
- - name: Download ARM64 GPU wheel from Gitea packages
- if: matrix.platform == 'linux/arm64'
- run: |
- mkdir -p src/paddle_ocr/wheels
- curl -L -o src/paddle_ocr/wheels/paddlepaddle_gpu-${{ env.PADDLE_VERSION }}-cp311-cp311-linux_aarch64.whl \
- "${{ env.WHEEL_BASE_URL }}/paddlepaddle-gpu-arm64/${{ env.PADDLE_VERSION }}/paddlepaddle_gpu-${{ env.PADDLE_VERSION }}-cp311-cp311-linux_aarch64.whl"
- ls -la src/paddle_ocr/wheels/
-
- - name: Build and push GPU image (${{ matrix.platform }})
+ - name: Build and push GPU image
uses: docker/build-push-action@v5
with:
context: src/paddle_ocr
file: src/paddle_ocr/Dockerfile.gpu
- platforms: ${{ matrix.platform }}
+ platforms: linux/amd64
push: true
tags: |
- ${{ needs.essential.outputs.image_gpu }}:${{ needs.essential.outputs.Version }}-${{ steps.arch.outputs.suffix }}
- ${{ needs.essential.outputs.image_gpu }}:${{ steps.arch.outputs.suffix }}
+ ${{ needs.essential.outputs.image_gpu }}:${{ needs.essential.outputs.Version }}
+ ${{ needs.essential.outputs.image_gpu }}:latest
- # Create multi-arch manifest for CPU image
- manifest_cpu:
- runs-on: ubuntu-latest
- needs: [essential, build_cpu]
- steps:
- - name: Login to Gitea Registry
- uses: docker/login-action@v3
- with:
- registry: ${{ needs.essential.outputs.repo }}
- username: username
- password: ${{ secrets.CI_READWRITE }}
-
- - name: Create multi-arch manifest (CPU)
- run: |
- docker buildx imagetools create -t ${{ needs.essential.outputs.image_cpu }}:latest \
- ${{ needs.essential.outputs.image_cpu }}:amd64 \
- ${{ needs.essential.outputs.image_cpu }}:arm64
- docker buildx imagetools create -t ${{ needs.essential.outputs.image_cpu }}:${{ needs.essential.outputs.Version }} \
- ${{ needs.essential.outputs.image_cpu }}:${{ needs.essential.outputs.Version }}-amd64 \
- ${{ needs.essential.outputs.image_cpu }}:${{ needs.essential.outputs.Version }}-arm64
-
- # Create multi-arch manifest for GPU image
- manifest_gpu:
- runs-on: ubuntu-latest
- needs: [essential, build_gpu]
- steps:
- - name: Login to Gitea Registry
- uses: docker/login-action@v3
- with:
- registry: ${{ needs.essential.outputs.repo }}
- username: username
- password: ${{ secrets.CI_READWRITE }}
-
- - name: Create multi-arch manifest (GPU)
- run: |
- docker buildx imagetools create -t ${{ needs.essential.outputs.image_gpu }}:latest \
- ${{ needs.essential.outputs.image_gpu }}:amd64 \
- ${{ needs.essential.outputs.image_gpu }}:arm64
- docker buildx imagetools create -t ${{ needs.essential.outputs.image_gpu }}:${{ needs.essential.outputs.Version }} \
- ${{ needs.essential.outputs.image_gpu }}:${{ needs.essential.outputs.Version }}-amd64 \
- ${{ needs.essential.outputs.image_gpu }}:${{ needs.essential.outputs.Version }}-arm64
-
- # EasyOCR image: Matrix build for amd64 and arm64
+ # EasyOCR CPU image (amd64 only)
build_easyocr:
runs-on: ubuntu-latest
needs: essential
- strategy:
- matrix:
- platform:
- - linux/amd64
- - linux/arm64
steps:
- name: Checkout
uses: actions/checkout@v4
- - name: Set up QEMU
- uses: docker/setup-qemu-action@v3
-
- name: Set up Docker Buildx
uses: docker/setup-buildx-action@v3
@@ -209,131 +108,25 @@ jobs:
username: username
password: ${{ secrets.CI_READWRITE }}
- - name: Get arch suffix
- id: arch
- run: |
- if [ "${{ matrix.platform }}" = "linux/amd64" ]; then
- echo "suffix=amd64" >> $GITHUB_OUTPUT
- else
- echo "suffix=arm64" >> $GITHUB_OUTPUT
- fi
-
- - name: Build and push EasyOCR image (${{ matrix.platform }})
+ - name: Build and push EasyOCR image
uses: docker/build-push-action@v5
with:
context: src/easyocr_service
file: src/easyocr_service/Dockerfile
- platforms: ${{ matrix.platform }}
+ platforms: linux/amd64
push: true
tags: |
- ${{ needs.essential.outputs.image_easyocr }}:${{ needs.essential.outputs.Version }}-${{ steps.arch.outputs.suffix }}
- ${{ needs.essential.outputs.image_easyocr }}:${{ steps.arch.outputs.suffix }}
+ ${{ needs.essential.outputs.image_easyocr }}:${{ needs.essential.outputs.Version }}
+ ${{ needs.essential.outputs.image_easyocr }}:latest
- # DocTR image: Matrix build for amd64 and arm64
- build_doctr:
- runs-on: ubuntu-latest
- needs: essential
- strategy:
- matrix:
- platform:
- - linux/amd64
- - linux/arm64
- steps:
- - name: Checkout
- uses: actions/checkout@v4
-
- - name: Set up QEMU
- uses: docker/setup-qemu-action@v3
-
- - name: Set up Docker Buildx
- uses: docker/setup-buildx-action@v3
-
- - name: Login to Gitea Registry
- uses: docker/login-action@v3
- with:
- registry: ${{ needs.essential.outputs.repo }}
- username: username
- password: ${{ secrets.CI_READWRITE }}
-
- - name: Get arch suffix
- id: arch
- run: |
- if [ "${{ matrix.platform }}" = "linux/amd64" ]; then
- echo "suffix=amd64" >> $GITHUB_OUTPUT
- else
- echo "suffix=arm64" >> $GITHUB_OUTPUT
- fi
-
- - name: Build and push DocTR image (${{ matrix.platform }})
- uses: docker/build-push-action@v5
- with:
- context: src/doctr_service
- file: src/doctr_service/Dockerfile
- platforms: ${{ matrix.platform }}
- push: true
- tags: |
- ${{ needs.essential.outputs.image_doctr }}:${{ needs.essential.outputs.Version }}-${{ steps.arch.outputs.suffix }}
- ${{ needs.essential.outputs.image_doctr }}:${{ steps.arch.outputs.suffix }}
-
- # Create multi-arch manifest for EasyOCR image
- manifest_easyocr:
- runs-on: ubuntu-latest
- needs: [essential, build_easyocr]
- steps:
- - name: Login to Gitea Registry
- uses: docker/login-action@v3
- with:
- registry: ${{ needs.essential.outputs.repo }}
- username: username
- password: ${{ secrets.CI_READWRITE }}
-
- - name: Create multi-arch manifest (EasyOCR)
- run: |
- docker buildx imagetools create -t ${{ needs.essential.outputs.image_easyocr }}:latest \
- ${{ needs.essential.outputs.image_easyocr }}:amd64 \
- ${{ needs.essential.outputs.image_easyocr }}:arm64
- docker buildx imagetools create -t ${{ needs.essential.outputs.image_easyocr }}:${{ needs.essential.outputs.Version }} \
- ${{ needs.essential.outputs.image_easyocr }}:${{ needs.essential.outputs.Version }}-amd64 \
- ${{ needs.essential.outputs.image_easyocr }}:${{ needs.essential.outputs.Version }}-arm64
-
- # Create multi-arch manifest for DocTR image
- manifest_doctr:
- runs-on: ubuntu-latest
- needs: [essential, build_doctr]
- steps:
- - name: Login to Gitea Registry
- uses: docker/login-action@v3
- with:
- registry: ${{ needs.essential.outputs.repo }}
- username: username
- password: ${{ secrets.CI_READWRITE }}
-
- - name: Create multi-arch manifest (DocTR)
- run: |
- docker buildx imagetools create -t ${{ needs.essential.outputs.image_doctr }}:latest \
- ${{ needs.essential.outputs.image_doctr }}:amd64 \
- ${{ needs.essential.outputs.image_doctr }}:arm64
- docker buildx imagetools create -t ${{ needs.essential.outputs.image_doctr }}:${{ needs.essential.outputs.Version }} \
- ${{ needs.essential.outputs.image_doctr }}:${{ needs.essential.outputs.Version }}-amd64 \
- ${{ needs.essential.outputs.image_doctr }}:${{ needs.essential.outputs.Version }}-arm64
-
- # EasyOCR GPU image: Matrix build for amd64 and arm64
- # PyTorch cu128 has wheels for both architectures
+ # EasyOCR GPU image (amd64 only)
build_easyocr_gpu:
runs-on: ubuntu-latest
needs: essential
- strategy:
- matrix:
- platform:
- - linux/amd64
- - linux/arm64
steps:
- name: Checkout
uses: actions/checkout@v4
- - name: Set up QEMU
- uses: docker/setup-qemu-action@v3
-
- name: Set up Docker Buildx
uses: docker/setup-buildx-action@v3
@@ -344,43 +137,25 @@ jobs:
username: username
password: ${{ secrets.CI_READWRITE }}
- - name: Get arch suffix
- id: arch
- run: |
- if [ "${{ matrix.platform }}" = "linux/amd64" ]; then
- echo "suffix=amd64" >> $GITHUB_OUTPUT
- else
- echo "suffix=arm64" >> $GITHUB_OUTPUT
- fi
-
- - name: Build and push EasyOCR GPU image (${{ matrix.platform }})
+ - name: Build and push EasyOCR GPU image
uses: docker/build-push-action@v5
with:
context: src/easyocr_service
file: src/easyocr_service/Dockerfile.gpu
- platforms: ${{ matrix.platform }}
+ platforms: linux/amd64
push: true
tags: |
- ${{ needs.essential.outputs.image_easyocr_gpu }}:${{ needs.essential.outputs.Version }}-${{ steps.arch.outputs.suffix }}
- ${{ needs.essential.outputs.image_easyocr_gpu }}:${{ steps.arch.outputs.suffix }}
+ ${{ needs.essential.outputs.image_easyocr_gpu }}:${{ needs.essential.outputs.Version }}
+ ${{ needs.essential.outputs.image_easyocr_gpu }}:latest
- # DocTR GPU image: Matrix build for amd64 and arm64
- # PyTorch cu128 has wheels for both architectures
- build_doctr_gpu:
+ # DocTR CPU image (amd64 only)
+ build_doctr:
runs-on: ubuntu-latest
needs: essential
- strategy:
- matrix:
- platform:
- - linux/amd64
- - linux/arm64
steps:
- name: Checkout
uses: actions/checkout@v4
- - name: Set up QEMU
- uses: docker/setup-qemu-action@v3
-
- name: Set up Docker Buildx
uses: docker/setup-buildx-action@v3
@@ -391,64 +166,42 @@ jobs:
username: username
password: ${{ secrets.CI_READWRITE }}
- - name: Get arch suffix
- id: arch
- run: |
- if [ "${{ matrix.platform }}" = "linux/amd64" ]; then
- echo "suffix=amd64" >> $GITHUB_OUTPUT
- else
- echo "suffix=arm64" >> $GITHUB_OUTPUT
- fi
+ - name: Build and push DocTR image
+ uses: docker/build-push-action@v5
+ with:
+ context: src/doctr_service
+ file: src/doctr_service/Dockerfile
+ platforms: linux/amd64
+ push: true
+ tags: |
+ ${{ needs.essential.outputs.image_doctr }}:${{ needs.essential.outputs.Version }}
+ ${{ needs.essential.outputs.image_doctr }}:latest
- - name: Build and push DocTR GPU image (${{ matrix.platform }})
+ # DocTR GPU image (amd64 only)
+ build_doctr_gpu:
+ runs-on: ubuntu-latest
+ needs: essential
+ steps:
+ - name: Checkout
+ uses: actions/checkout@v4
+
+ - name: Set up Docker Buildx
+ uses: docker/setup-buildx-action@v3
+
+ - name: Login to Gitea Registry
+ uses: docker/login-action@v3
+ with:
+ registry: ${{ needs.essential.outputs.repo }}
+ username: username
+ password: ${{ secrets.CI_READWRITE }}
+
+ - name: Build and push DocTR GPU image
uses: docker/build-push-action@v5
with:
context: src/doctr_service
file: src/doctr_service/Dockerfile.gpu
- platforms: ${{ matrix.platform }}
+ platforms: linux/amd64
push: true
tags: |
- ${{ needs.essential.outputs.image_doctr_gpu }}:${{ needs.essential.outputs.Version }}-${{ steps.arch.outputs.suffix }}
- ${{ needs.essential.outputs.image_doctr_gpu }}:${{ steps.arch.outputs.suffix }}
-
- # Create multi-arch manifest for EasyOCR GPU image
- manifest_easyocr_gpu:
- runs-on: ubuntu-latest
- needs: [essential, build_easyocr_gpu]
- steps:
- - name: Login to Gitea Registry
- uses: docker/login-action@v3
- with:
- registry: ${{ needs.essential.outputs.repo }}
- username: username
- password: ${{ secrets.CI_READWRITE }}
-
- - name: Create multi-arch manifest (EasyOCR GPU)
- run: |
- docker buildx imagetools create -t ${{ needs.essential.outputs.image_easyocr_gpu }}:latest \
- ${{ needs.essential.outputs.image_easyocr_gpu }}:amd64 \
- ${{ needs.essential.outputs.image_easyocr_gpu }}:arm64
- docker buildx imagetools create -t ${{ needs.essential.outputs.image_easyocr_gpu }}:${{ needs.essential.outputs.Version }} \
- ${{ needs.essential.outputs.image_easyocr_gpu }}:${{ needs.essential.outputs.Version }}-amd64 \
- ${{ needs.essential.outputs.image_easyocr_gpu }}:${{ needs.essential.outputs.Version }}-arm64
-
- # Create multi-arch manifest for DocTR GPU image
- manifest_doctr_gpu:
- runs-on: ubuntu-latest
- needs: [essential, build_doctr_gpu]
- steps:
- - name: Login to Gitea Registry
- uses: docker/login-action@v3
- with:
- registry: ${{ needs.essential.outputs.repo }}
- username: username
- password: ${{ secrets.CI_READWRITE }}
-
- - name: Create multi-arch manifest (DocTR GPU)
- run: |
- docker buildx imagetools create -t ${{ needs.essential.outputs.image_doctr_gpu }}:latest \
- ${{ needs.essential.outputs.image_doctr_gpu }}:amd64 \
- ${{ needs.essential.outputs.image_doctr_gpu }}:arm64
- docker buildx imagetools create -t ${{ needs.essential.outputs.image_doctr_gpu }}:${{ needs.essential.outputs.Version }} \
- ${{ needs.essential.outputs.image_doctr_gpu }}:${{ needs.essential.outputs.Version }}-amd64 \
- ${{ needs.essential.outputs.image_doctr_gpu }}:${{ needs.essential.outputs.Version }}-arm64
+ ${{ needs.essential.outputs.image_doctr_gpu }}:${{ needs.essential.outputs.Version }}
+ ${{ needs.essential.outputs.image_doctr_gpu }}:latest
diff --git a/docs/07_anexo_a.md b/docs/07_anexo_a.md
index 4ccb8b6..47b21e4 100644
--- a/docs/07_anexo_a.md
+++ b/docs/07_anexo_a.md
@@ -8,61 +8,186 @@ El código fuente completo y los datos utilizados en este trabajo están disponi
El repositorio incluye:
-- **Notebooks de experimentación**: Código completo de los experimentos realizados
-- **Scripts de evaluación**: Herramientas para evaluar modelos OCR
+- **Servicios OCR dockerizados**: PaddleOCR, DocTR, EasyOCR con soporte GPU
+- **Scripts de evaluación**: Herramientas para evaluar y comparar modelos OCR
+- **Scripts de ajuste**: Ray Tune con Optuna para optimización de hiperparámetros
- **Dataset**: Imágenes y textos de referencia utilizados
-- **Resultados**: Archivos CSV con los resultados de los 64 trials de Ray Tune
+- **Resultados**: Archivos CSV con los resultados de los 64 trials por servicio
## A.2 Estructura del Repositorio
-```mermaid
----
-title: "Estructura del repositorio del proyecto"
----
-flowchart LR
- root["MastersThesis/"] --> docs["docs/"]
- root --> src["src/"]
- root --> instructions["instructions/"]
- root --> scripts["Scripts generación"]
-
- src --> nb1["paddle_ocr_fine_tune_unir_raytune.ipynb"]
- src --> py1["paddle_ocr_tuning.py"]
- src --> csv["raytune_paddle_subproc_results_*.csv"]
-
- scripts --> gen1["generate_mermaid_figures.py"]
- scripts --> gen2["apply_content.py"]
```
-
-**Descripción de componentes:**
-
-- **docs/**: Capítulos de la tesis en Markdown (estructura UNIR)
-- **src/**: Código fuente de experimentación
- - `paddle_ocr_fine_tune_unir_raytune.ipynb`: Notebook principal con 64 trials Ray Tune
- - `paddle_ocr_tuning.py`: Script CLI para evaluación OCR
- - `raytune_paddle_subproc_results_20251207_192320.csv`: Resultados de optimización
-- **instructions/**: Plantilla e instrucciones UNIR
-- **Scripts de generación**: `generate_mermaid_figures.py` y `apply_content.py` para generar el documento TFM
+MastersThesis/
+├── docs/ # Documentación de la tesis
+│ └── metrics/ # Métricas de rendimiento OCR
+│ ├── metrics.md # Resumen comparativo
+│ ├── metrics_paddle.md # Resultados PaddleOCR
+│ ├── metrics_doctr.md # Resultados DocTR
+│ └── metrics_easyocr.md # Resultados EasyOCR
+├── src/
+│ ├── paddle_ocr/ # Servicio PaddleOCR
+│ │ ├── Dockerfile.gpu # Imagen Docker GPU
+│ │ ├── Dockerfile.cpu # Imagen Docker CPU
+│ │ ├── docker-compose.yml # Configuración Docker
+│ │ └── main.py # API FastAPI
+│ ├── doctr_service/ # Servicio DocTR
+│ │ ├── Dockerfile.gpu
+│ │ ├── docker-compose.yml
+│ │ └── main.py
+│ ├── easyocr_service/ # Servicio EasyOCR
+│ │ ├── Dockerfile.gpu
+│ │ ├── docker-compose.yml
+│ │ └── main.py
+│ ├── dataset/ # Dataset de evaluación
+│ ├── raytune_ocr.py # Utilidades compartidas Ray Tune
+│ └── results/ # Resultados de ajuste CSV
+└── .gitea/workflows/ci.yaml # Pipeline CI/CD
+```
## A.3 Requisitos de Software
-Para reproducir los experimentos se requieren las siguientes dependencias:
+### Sistema de Desarrollo
+
+| Componente | Especificación |
+|------------|----------------|
+| Sistema Operativo | Ubuntu 24.04.3 LTS |
+| CPU | AMD Ryzen 7 5800H |
+| RAM | 16 GB DDR4 |
+| GPU | NVIDIA RTX 3060 Laptop (5.66 GB VRAM) |
+| CUDA | 12.4 |
+
+### Dependencias
| Componente | Versión |
|------------|---------|
-| Python | 3.11.9 |
-| PaddlePaddle | 3.2.2 |
-| PaddleOCR | 3.3.2 |
-| Ray | 2.52.1 |
-| Optuna | 4.6.0 |
-| jiwer | (última versión) |
-| PyMuPDF | (última versión) |
+| Python | 3.11 |
+| Docker | 24+ |
+| NVIDIA Container Toolkit | Requerido para GPU |
+| Ray | 2.52+ |
+| Optuna | 4.6+ |
-## A.4 Instrucciones de Ejecución
+## A.4 Instrucciones de Ejecución de Servicios OCR
-1. Clonar el repositorio
-2. Instalar dependencias: `pip install -r requirements.txt`
-3. Ejecutar el notebook `src/paddle_ocr_fine_tune_unir_raytune.ipynb`
+### PaddleOCR (Puerto 8002)
-## A.5 Licencia
+```bash
+cd src/paddle_ocr
+
+# GPU (recomendado)
+docker compose up -d
+
+# CPU (más lento, 126x)
+docker compose -f docker-compose.cpu-registry.yml up -d
+```
+
+### DocTR (Puerto 8003)
+
+```bash
+cd src/doctr_service
+
+# GPU
+docker compose up -d
+```
+
+### EasyOCR (Puerto 8002)
+
+```bash
+cd src/easyocr_service
+
+# GPU
+docker compose up -d
+```
+
+### Verificar Estado del Servicio
+
+```bash
+# Verificar salud del servicio
+curl http://localhost:8002/health
+
+# Respuesta esperada:
+# {"status": "ok", "model_loaded": true, "gpu_name": "NVIDIA GeForce RTX 3060"}
+```
+
+## A.5 Uso de la API OCR
+
+### Evaluar Dataset Completo
+
+```bash
+# PaddleOCR - Evaluación completa
+curl -X POST http://localhost:8002/evaluate_full \
+ -H "Content-Type: application/json" \
+ -d '{
+ "pdf_folder": "/app/dataset",
+ "save_output": true
+ }'
+```
+
+### Evaluar con Hiperparámetros Optimizados
+
+```bash
+# PaddleOCR con configuración óptima
+curl -X POST http://localhost:8002/evaluate_full \
+ -H "Content-Type: application/json" \
+ -d '{
+ "pdf_folder": "/app/dataset",
+ "use_doc_orientation_classify": true,
+ "use_doc_unwarping": false,
+ "textline_orientation": true,
+ "text_det_thresh": 0.0462,
+ "text_det_box_thresh": 0.4862,
+ "text_det_unclip_ratio": 0.0,
+ "text_rec_score_thresh": 0.5658,
+ "save_output": true
+ }'
+```
+
+## A.6 Ajuste de Hiperparámetros con Ray Tune
+
+### Ejecutar Ajuste
+
+```bash
+cd src
+
+# Activar entorno virtual
+source ../.venv/bin/activate
+
+# PaddleOCR (64 muestras)
+python -c "
+from raytune_ocr import *
+
+ports = [8002]
+check_workers(ports, 'PaddleOCR')
+trainable = create_trainable(ports, paddle_ocr_payload)
+results = run_tuner(trainable, PADDLE_OCR_SEARCH_SPACE, num_samples=64)
+analyze_results(results, prefix='raytune_paddle', config_keys=PADDLE_OCR_CONFIG_KEYS)
+"
+```
+
+### Servicios y Puertos
+
+| Servicio | Puerto | Script de Ajuste |
+|----------|--------|------------------|
+| PaddleOCR | 8002 | `paddle_ocr_payload` |
+| DocTR | 8003 | `doctr_payload` |
+| EasyOCR | 8002 | `easyocr_payload` |
+
+## A.7 Métricas de Rendimiento
+
+Los resultados detallados de las evaluaciones y ajustes de hiperparámetros se encuentran en:
+
+- [Métricas Generales](metrics/metrics.md) - Comparativa de los tres servicios
+- [PaddleOCR](metrics/metrics_paddle.md) - Mejor precisión (7.72% CER)
+- [DocTR](metrics/metrics_doctr.md) - Más rápido (0.50s/página)
+- [EasyOCR](metrics/metrics_easyocr.md) - Balance intermedio
+
+### Resumen de Resultados
+
+| Servicio | CER Base | CER Ajustado | Mejora |
+|----------|----------|--------------|--------|
+| **PaddleOCR** | 8.85% | **7.72%** | 12.8% |
+| DocTR | 12.06% | 12.07% | 0% |
+| EasyOCR | 11.23% | 11.14% | 0.8% |
+
+## A.8 Licencia
El código se distribuye bajo licencia MIT.
diff --git a/docs/metrics.md b/docs/metrics.md
deleted file mode 100644
index 3061ab2..0000000
--- a/docs/metrics.md
+++ /dev/null
@@ -1,289 +0,0 @@
-# PaddleOCR Performance Metrics: CPU vs GPU
-
-**Benchmark Date:** 2026-01-17
-**Updated:** 2026-01-17 (GPU fix applied)
-**Test Dataset:** 5 pages (pages 5-10)
-**Platform:** Linux (NVIDIA GB10 GPU, 119.70 GB VRAM)
-
-## Executive Summary
-
-| Metric | GPU | CPU | Difference |
-|--------|-----|-----|------------|
-| **Time per Page** | 0.86s | 84.25s | GPU is **97.6x faster** |
-| **Total Time (5 pages)** | 4.63s | 421.59s | 7 min saved |
-| **CER (Character Error Rate)** | 100%* | 3.96% | *Recognition issue |
-| **WER (Word Error Rate)** | 100%* | 13.65% | *Recognition issue |
-
-> **UPDATE (2026-01-17):** GPU CUDA support fixed! PaddlePaddle wheel rebuilt with PTX for Blackwell forward compatibility. GPU inference now runs at full speed (0.86s/page vs 84s CPU). However, 100% error rate persists - this appears to be a separate OCR model/recognition issue, not CUDA-related.
-
-## Performance Comparison
-
-### Processing Speed (Time per Page)
-
-```mermaid
-xychart-beta
- title "Processing Time per Page (seconds)"
- x-axis ["GPU", "CPU"]
- y-axis "Seconds" 0 --> 90
- bar [0.86, 84.25]
-```
-
-### Speed Ratio Visualization
-
-```mermaid
-pie showData
- title "Relative Processing Time"
- "GPU (1x)" : 1
- "CPU (97.6x slower)" : 97.6
-```
-
-### Total Benchmark Time
-
-```mermaid
-xychart-beta
- title "Total Time for 5 Pages (seconds)"
- x-axis ["GPU", "CPU"]
- y-axis "Seconds" 0 --> 450
- bar [4.63, 421.59]
-```
-
-## OCR Accuracy Metrics (CPU Container - Baseline Config)
-
-```mermaid
-xychart-beta
- title "OCR Error Rates (CPU Container)"
- x-axis ["CER", "WER"]
- y-axis "Error Rate %" 0 --> 20
- bar [3.96, 13.65]
-```
-
-## Architecture Overview
-
-```mermaid
-flowchart TB
- subgraph Client
- A[Test Script
benchmark.py]
- end
-
- subgraph "Docker Containers"
- subgraph GPU["GPU Container :8000"]
- B[FastAPI Server]
- C[PaddleOCR
CUDA Backend]
- D[NVIDIA GB10
119.70 GB VRAM]
- end
-
- subgraph CPU["CPU Container :8002"]
- E[FastAPI Server]
- F[PaddleOCR
CPU Backend]
- G[ARM64 CPU]
- end
- end
-
- subgraph Storage
- H[(Dataset
45 PDFs)]
- end
-
- A -->|REST API| B
- A -->|REST API| E
- B --> C --> D
- E --> F --> G
- C --> H
- F --> H
-```
-
-## Benchmark Workflow
-
-```mermaid
-sequenceDiagram
- participant T as Test Script
- participant G as GPU Container
- participant C as CPU Container
-
- T->>G: Health Check
- G-->>T: Ready (model_loaded: true)
-
- T->>C: Health Check
- C-->>T: Ready (model_loaded: true)
-
- Note over T,G: GPU Benchmark
- T->>G: Warmup (1 page)
- G-->>T: Complete
- T->>G: POST /evaluate (Baseline)
- G-->>T: 4.63s total (0.86s/page)
- T->>G: POST /evaluate (Optimized)
- G-->>T: 4.63s total (0.86s/page)
-
- Note over T,C: CPU Benchmark
- T->>C: Warmup (1 page)
- C-->>T: Complete (~84s)
- T->>C: POST /evaluate (Baseline)
- C-->>T: 421.59s total (84.25s/page)
-```
-
-## Performance Timeline
-
-```mermaid
-gantt
- title Processing Time Comparison (5 Pages)
- dateFormat ss
- axisFormat %S s
-
- section GPU
- All 5 pages :gpu, 00, 5s
-
- section CPU
- Page 1 :cpu1, 00, 84s
- Page 2 :cpu2, after cpu1, 84s
- Page 3 :cpu3, after cpu2, 84s
- Page 4 :cpu4, after cpu3, 84s
- Page 5 :cpu5, after cpu4, 84s
-```
-
-## Container Specifications
-
-```mermaid
-mindmap
- root((PaddleOCR
Containers))
- GPU Container
- Port 8000
- CUDA Enabled
- NVIDIA GB10
- 119.70 GB VRAM
- 0.86s per page
- CPU Container
- Port 8002
- ARM64 Architecture
- No CUDA
- 84.25s per page
- 3.96% CER
-```
-
-## Key Findings
-
-### Speed Analysis
-
-1. **GPU Acceleration Impact**: The GPU container processes pages **97.6x faster** than the CPU container
-2. **Throughput**: GPU can process ~70 pages/minute vs CPU at ~0.7 pages/minute
-3. **Scalability**: For large document batches, GPU provides significant time savings
-
-### Accuracy Analysis
-
-| Configuration | CER | WER | Notes |
-|--------------|-----|-----|-------|
-| CPU Baseline | 3.96% | 13.65% | Working correctly |
-| CPU Optimized | Error | Error | Server error (needs investigation) |
-| GPU Baseline | 100%* | 100%* | Recognition issue* |
-| GPU Optimized | 100%* | 100%* | Recognition issue* |
-
-> *GPU accuracy metrics require investigation - speed benchmarks are valid
-
-## Recommendations
-
-```mermaid
-flowchart LR
- A{Use Case?}
- A -->|High Volume
Speed Critical| B[GPU Container]
- A -->|Low Volume
Cost Sensitive| C[CPU Container]
- A -->|Development
Testing| D[CPU Container]
-
- B --> E[0.86s/page
Best for production]
- C --> F[84.25s/page
Lower infrastructure cost]
- D --> G[No GPU required
Easy local setup]
-```
-
-## Raw Benchmark Data
-
-```json
-{
- "timestamp": "2026-01-17T17:25:55.541442",
- "containers": {
- "GPU": {
- "url": "http://localhost:8000",
- "tests": {
- "Baseline": {
- "CER": 1.0,
- "WER": 1.0,
- "PAGES": 5,
- "TIME_PER_PAGE": 0.863,
- "TOTAL_TIME": 4.63
- }
- }
- },
- "CPU": {
- "url": "http://localhost:8002",
- "tests": {
- "Baseline": {
- "CER": 0.0396,
- "WER": 0.1365,
- "PAGES": 5,
- "TIME_PER_PAGE": 84.249,
- "TOTAL_TIME": 421.59
- }
- }
- }
- }
-}
-```
-
-## GPU Issue Analysis
-
-### Root Cause Identified (RESOLVED)
-
-The GPU container originally returned 100% error rate due to a **CUDA architecture mismatch**:
-
-```
-W0117 16:55:35.199092 gpu_resources.cc:106] The GPU compute capability in your
-current machine is 121, which is not supported by Paddle
-```
-
-| Issue | Details |
-|-------|---------|
-| **GPU** | NVIDIA GB10 (Compute Capability 12.1 - Blackwell) |
-| **Original Wheel** | Built for `CUDA_ARCH=90` (sm_90 - Hopper) without PTX |
-| **Result** | Detection kernels couldn't execute on Blackwell architecture |
-
-### Solution Applied ✅
-
-**1. Rebuilt PaddlePaddle wheel with PTX forward compatibility:**
-
-The `Dockerfile.build-paddle` was updated to generate PTX code in addition to cubin:
-
-```dockerfile
--DCUDA_NVCC_FLAGS="-gencode=arch=compute_90,code=sm_90 -gencode=arch=compute_90,code=compute_90"
-```
-
-This generates:
-- `sm_90` cubin (binary for Hopper)
-- `compute_90` PTX (portable code for JIT compilation on newer architectures)
-
-**2. cuBLAS symlinks** (already in Dockerfile.gpu):
-
-```dockerfile
-ln -sf /usr/local/cuda/lib64/libcublas.so.12 /usr/local/cuda/lib64/libcublas.so
-```
-
-### Verification Results
-
-```
-PaddlePaddle version: 0.0.0 (custom GPU build)
-CUDA available: True
-GPU count: 1
-GPU name: NVIDIA GB10
-Tensor on GPU: Place(gpu:0)
-GPU OCR: Functional ✅
-```
-
-The PTX code is JIT-compiled at runtime for the GB10's compute capability 12.1.
-
-### Build Artifacts
-
-- **Wheel**: `paddlepaddle_gpu-3.0.0-cp311-cp311-linux_aarch64.whl` (418 MB)
-- **Build time**: ~40 minutes (with ccache)
-- **Location**: `src/paddle_ocr/wheels/`
-
-## Next Steps
-
-1. ~~**Rebuild GPU wheel**~~ ✅ Done - PTX-enabled wheel built
-2. **Re-run benchmarks** - Verify accuracy metrics with fixed GPU
-3. **Fix CPU optimized config** - Server error on optimized configuration needs debugging
-4. **Memory profiling** - Monitor GPU/CPU memory usage during processing
diff --git a/docs/metrics/metrics.md b/docs/metrics/metrics.md
new file mode 100644
index 0000000..8f636e5
--- /dev/null
+++ b/docs/metrics/metrics.md
@@ -0,0 +1,211 @@
+# Métricas de Rendimiento OCR
+
+**Fecha de Benchmark:** 2026-01-19
+**Dataset de Prueba:** 45 páginas (2 PDFs)
+
+## Especificaciones del Sistema
+
+| Componente | Especificación |
+|------------|----------------|
+| **Sistema Operativo** | Ubuntu 24.04.3 LTS (Noble) |
+| **Kernel** | 6.14.0-37-generic |
+| **CPU** | AMD Ryzen 7 5800H with Radeon Graphics |
+| **RAM** | 16 GB DDR4 |
+| **GPU** | NVIDIA GeForce RTX 3060 Laptop GPU |
+| **VRAM** | 5.66 GB |
+| **CUDA** | 12.4 |
+
+## Justificación de Ejecución Local vs Cloud
+
+### Costos de Cloud GPU
+
+| Plataforma | GPU | Costo/Hora | Costo Mensual |
+|------------|-----|------------|---------------|
+| **AWS EC2 g4dn.xlarge** | NVIDIA T4 (16 GB) | $0.526 | ~$384 |
+| **Google Colab Pro** | T4/P100 | ~$1.30 | $10 + CU extras |
+| **Google Colab Pro+** | T4/V100/A100 | ~$1.30 | $50 + CU extras |
+
+### Análisis de Costos para Este Proyecto
+
+| Tarea | Tiempo GPU | Costo AWS | Costo Colab Pro |
+|-------|------------|-----------|-----------------|
+| Ajuste hiperparámetros (64×3 trials) | ~3 horas | ~$1.58 | ~$3.90 |
+| Evaluación completa (45 páginas) | ~5 min | ~$0.04 | ~$0.11 |
+| Desarrollo/debug (20 horas/mes) | 20 horas | ~$10.52 | ~$26.00 |
+
+### Ventajas de Ejecución Local
+
+1. **Costo cero de GPU**: La RTX 3060 ya está disponible en el equipo de desarrollo
+2. **Sin límites de tiempo**: AWS y Colab tienen timeouts de sesión
+3. **Acceso instantáneo**: Sin tiempo de aprovisionamiento de instancias
+4. **Almacenamiento local**: Dataset y resultados en disco sin costos de transferencia
+5. **Iteración rápida**: Reinicio inmediato de contenedores Docker
+
+### Conclusión
+
+Para un proyecto de investigación con múltiples iteraciones de ajuste de hiperparámetros y desarrollo, **la ejecución local ahorra ~$10-50/mes** comparado con cloud, además de ofrecer mayor flexibilidad y velocidad de iteración
+
+## Resumen Ejecutivo
+
+| Servicio | CER | WER | Tiempo/Página | Tiempo Total | VRAM |
+|----------|-----|-----|---------------|--------------|------|
+| **PaddleOCR (Mobile)** | **7.76%** | **11.62%** | 0.58s | 32.0s | 0.06 GB |
+| EasyOCR | 11.23% | 36.36% | 1.88s | 88.5s | ~2 GB |
+| DocTR | 12.06% | 42.01% | 0.50s | 28.4s | ~1 GB |
+
+> **Ganador:** PaddleOCR (Mobile) - Mejor precisión (7.76% CER) con velocidad competitiva.
+
+## Comparación de Servicios OCR
+
+### Comparación de Precisión (CER - menor es mejor)
+
+```mermaid
+xychart-beta
+ title "Tasa de Error de Caracteres por Servicio"
+ x-axis ["PaddleOCR", "EasyOCR", "DocTR"]
+ y-axis "CER %" 0 --> 15
+ bar [7.76, 11.23, 12.06]
+```
+
+### Comparación de Velocidad (Tiempo por Página)
+
+```mermaid
+xychart-beta
+ title "Tiempo de Procesamiento por Página (segundos)"
+ x-axis ["DocTR", "PaddleOCR", "EasyOCR"]
+ y-axis "Segundos" 0 --> 2
+ bar [0.50, 0.58, 1.88]
+```
+
+### Flujo de Recomendación de Servicio
+
+```mermaid
+flowchart LR
+ A{Prioridad?}
+ A -->|Precisión| B[PaddleOCR]
+ A -->|Velocidad| C[DocTR]
+ A -->|Balance| B
+ B --> D["7.76% CER
0.58s/página"]
+ C --> E["12.06% CER
0.50s/página"]
+```
+
+### Hallazgos Clave
+
+1. **Mejor Precisión**: PaddleOCR logra las tasas de error más bajas (7.76% CER, 11.62% WER)
+2. **Mejor Velocidad**: DocTR es el más rápido (0.50s/página), pero 55% menos preciso que PaddleOCR
+3. **EasyOCR**: El más lento (3.8x más lento que PaddleOCR) con precisión intermedia
+4. **Eficiencia VRAM**: PaddleOCR Mobile usa solo 0.06 GB
+
+## Análisis de Errores (del debugset)
+
+### PaddleOCR (Mejor - 7.76% CER)
+- **Fortalezas**: Preserva estructura de líneas, maneja bien acentos españoles
+- **Problemas**: Errores menores de espaciado, diferencias ocasionales de mayúsculas en acentos
+- **Mejorable**: Sí - el ajuste de hiperparámetros probablemente ayude
+
+### DocTR (Peor WER - 42.01%)
+- **Problema Crítico**: Colapsa todo el texto en líneas únicas (pierde estructura)
+- **Problema de Acentos**: Omite diacríticos ("Indice" vs "Índice")
+- **Mejorable**: Parcialmente - el problema de estructura puede ser a nivel de modelo
+
+### EasyOCR (36.36% WER)
+- **Problema Crítico**: Inserciones espurias de caracteres (";", "g", "0", "1")
+- **Pérdida de Estructura**: Saltos de línea no preservados
+- **Mejorable**: Sí - umbrales de detección demasiado sensibles
+
+## Comparación de Modelos PaddleOCR (RTX 3060)
+
+| Métrica | Modelos Server | Modelos Mobile | Ganador |
+|---------|----------------|----------------|---------|
+| **Tiempo** | 2.47s | 1.08s | Mobile (2.3x más rápido) |
+| **CER** | 1.82% | 1.42% | Mobile |
+| **WER** | 16.14% | 12.20% | Mobile |
+| **VRAM** | 5.3 GB (OOM en página 2) | 0.06 GB | Mobile |
+| **Multi-página** | No (OOM) | Sí | Mobile |
+
+> **Conclusión:** Se recomiendan los modelos Mobile - más rápidos, más precisos, caben en VRAM.
+
+## Rendimiento CPU vs GPU (PaddleOCR)
+
+Datos de `raytune_paddle_subproc_results_20251207_192320.csv` (CPU) vs RTX 3060 (GPU):
+
+| Métrica | CPU | GPU (RTX 3060) | Aceleración |
+|---------|-----|----------------|-------------|
+| **Tiempo/Página** | 69.4s | 0.55s | **126x más rápido** |
+| **Mejor CER** | 1.15% | 0.79% | GPU mejor |
+| **45 páginas** | ~52 min | ~25 seg | **126x más rápido** |
+
+```mermaid
+xychart-beta
+ title "Tiempo de Procesamiento por Página: CPU vs GPU"
+ x-axis ["CPU", "GPU (RTX 3060)"]
+ y-axis "Segundos" 0 --> 80
+ bar [69.4, 0.55]
+```
+
+> **Conclusión:** GPU es esencial para uso práctico de OCR. El procesamiento en CPU es 126x más lento, haciéndolo impráctico para procesamiento por lotes.
+
+## Datos Crudos del Benchmark
+
+```json
+{
+ "timestamp": "2026-01-19T11:00:00.000000",
+ "platform": {
+ "gpu": "NVIDIA GeForce RTX 3060 Laptop GPU",
+ "vram": "5.66 GB",
+ "cuda": "12.4"
+ },
+ "services": {
+ "PaddleOCR_Mobile": {
+ "port": 8002,
+ "models": {"det": "PP-OCRv5_mobile_det", "rec": "PP-OCRv5_mobile_rec"},
+ "vram_used": "0.06 GB",
+ "results": {
+ "CER": 0.0776,
+ "WER": 0.1162,
+ "PAGES": 45,
+ "TIME_PER_PAGE": 0.58,
+ "TOTAL_TIME": 32.0
+ }
+ },
+ "DocTR": {
+ "port": 8003,
+ "models": {"det": "db_resnet50", "rec": "crnn_vgg16_bn"},
+ "vram_used": "~1 GB",
+ "results": {
+ "CER": 0.1206,
+ "WER": 0.4201,
+ "PAGES": 45,
+ "TIME_PER_PAGE": 0.50,
+ "TOTAL_TIME": 28.4
+ }
+ },
+ "EasyOCR": {
+ "port": 8002,
+ "languages": ["es", "en"],
+ "vram_used": "~2 GB",
+ "results": {
+ "CER": 0.1123,
+ "WER": 0.3636,
+ "PAGES": 45,
+ "TIME_PER_PAGE": 1.88,
+ "TOTAL_TIME": 88.5
+ }
+ }
+ }
+}
+```
+
+## Resultados de Ajuste de Hiperparámetros
+
+Resultados individuales de ajuste por servicio (64 muestras cada uno, páginas 5-10):
+
+- [Resultados de Ajuste PaddleOCR](metrics_paddle.md)
+- [Resultados de Ajuste DocTR](metrics_doctr.md)
+- [Resultados de Ajuste EasyOCR](metrics_easyocr.md)
+
+## Próximos Pasos
+
+1. ~~Ajuste de Hiperparámetros~~ - Completado (64 muestras por servicio)
+2. **Evaluación del Dataset Completo** - Ejecutar mejores configuraciones en las 45 páginas
+3. **Comparar** - Rendimiento base vs ajustado en dataset completo
diff --git a/docs/metrics/metrics_doctr.md b/docs/metrics/metrics_doctr.md
new file mode 100644
index 0000000..6dab47c
--- /dev/null
+++ b/docs/metrics/metrics_doctr.md
@@ -0,0 +1,104 @@
+# Resultados de Ajuste de Hiperparámetros DocTR
+
+**Fecha de Ajuste:** 2026-01-19
+**Plataforma:** NVIDIA RTX 3060 Laptop GPU
+**Muestras:** 64
+**Páginas de Prueba:** 5-10 (primer documento)
+
+### ¿Por Qué Solo 5 Páginas?
+
+Usamos solo 5 páginas (páginas 5-10) para el ajuste de hiperparámetros porque:
+
+1. **Velocidad**: 64 pruebas × 5 páginas = 320 evaluaciones de página. Con 45 páginas, serían 2,880 evaluaciones (~9x más tiempo)
+2. **Eficiencia de recursos**: Cada prueba toma ~2-20 segundos en GPU
+
+**Riesgo de Sobreajuste**: El ajuste de hiperparámetros en un subconjunto pequeño PUEDE causar sobreajuste. Nuestros resultados confirman esto:
+- Subconjunto de ajuste: **38% mejora** (7.43% CER)
+- Dataset completo: **0% mejora** (12.07% CER)
+
+La falta total de mejora en el dataset completo indica sobreajuste severo a las páginas 5-10, combinado con limitaciones a nivel de modelo (manejo de diacríticos, estructura de líneas) que los hiperparámetros no pueden corregir.
+
+## Evaluación del Dataset Completo (45 páginas)
+
+| Métrica | Base | Ajustado | Mejora |
+|---------|------|----------|--------|
+| **CER** | 12.06% | 12.07% | **0%** |
+| **WER** | 42.01% | 42.26% | **0%** |
+| Tiempo/Página | 0.33s | 0.34s | - |
+
+> **Nota:** El ajuste no generalizó al dataset completo. Los problemas de DocTR parecen ser a nivel de modelo (diacríticos, estructura de líneas).
+
+## Resultados del Subconjunto de Ajuste (páginas 5-10)
+
+| Métrica | Base | Ajustado | Mejora |
+|---------|------|----------|--------|
+| **CER** | 12.06% | **7.43%** | **38%** |
+| **WER** | 42.01% | **35.23%** | **16%** |
+
+> Nota: Las mejoras en el subconjunto de ajuste no se transfirieron al dataset completo.
+
+## Mejor Configuración Encontrada
+
+```json
+{
+ "assume_straight_pages": true,
+ "straighten_pages": false,
+ "preserve_aspect_ratio": false,
+ "symmetric_pad": false,
+ "disable_page_orientation": true,
+ "disable_crop_orientation": false,
+ "resolve_lines": true,
+ "resolve_blocks": false,
+ "paragraph_break": 0.0977
+}
+```
+
+## Hallazgos Clave
+
+1. **straighten_pages: false** - ¡Crítico! Configurarlo en true causa ~79% CER (catastrófico)
+2. **assume_straight_pages: true** - Funciona bien con escaneos de documentos rectos
+3. **resolve_lines: true** - Ayuda a mantener la estructura de líneas
+4. **disable_page_orientation: true** - Evita rotación innecesaria
+
+## Impacto de Parámetros
+
+Parámetros que mejoraron la precisión:
+- `straighten_pages=False` absolutamente crítico
+- `assume_straight_pages=True` en los mejores resultados
+- `resolve_lines=True` mantiene la estructura del texto
+
+Parámetros que perjudicaron la precisión:
+- `straighten_pages=True` catastróficamente malo (~79% CER)
+- `resolve_blocks=True` ligeramente peor que False
+
+## Limitaciones Conocidas
+
+Incluso con ajuste, DocTR todavía tiene problemas:
+- Omite diacríticos (tildes) - probablemente problema a nivel de modelo
+- Todavía tiene mayor WER que PaddleOCR debido a problemas de estructura
+
+## Evaluación del Dataset Completo
+
+**Estado:** Completado
+
+```bash
+curl -X POST http://localhost:8003/evaluate_full \
+ -H "Content-Type: application/json" \
+ -d '{
+ "pdf_folder": "/app/dataset",
+ "assume_straight_pages": true,
+ "straighten_pages": false,
+ "preserve_aspect_ratio": false,
+ "symmetric_pad": false,
+ "disable_page_orientation": true,
+ "disable_crop_orientation": false,
+ "resolve_lines": true,
+ "resolve_blocks": false,
+ "paragraph_break": 0.0977,
+ "save_output": true
+ }'
+```
+
+**Resultado:** CER 12.07%, WER 42.26%, 0.34s/página (sin mejora sobre la base)
+
+**Conclusión:** Los problemas de precisión de DocTR son a nivel de modelo, no ajustables por hiperparámetros.
diff --git a/docs/metrics/metrics_easyocr.md b/docs/metrics/metrics_easyocr.md
new file mode 100644
index 0000000..2c6ab51
--- /dev/null
+++ b/docs/metrics/metrics_easyocr.md
@@ -0,0 +1,113 @@
+# Resultados de Ajuste de Hiperparámetros EasyOCR
+
+**Fecha de Ajuste:** 2026-01-19
+**Plataforma:** NVIDIA RTX 3060 Laptop GPU
+**Muestras:** 64
+**Páginas de Prueba:** 5-10 (primer documento)
+
+### ¿Por Qué Solo 5 Páginas?
+
+Usamos solo 5 páginas (páginas 5-10) para el ajuste de hiperparámetros porque:
+
+1. **Velocidad**: 64 pruebas × 5 páginas = 320 evaluaciones de página. Con 45 páginas, serían 2,880 evaluaciones (~9x más tiempo)
+2. **Eficiencia de recursos**: Cada prueba toma ~10-20 segundos en GPU
+
+**Riesgo de Sobreajuste**: El ajuste de hiperparámetros en un subconjunto pequeño PUEDE causar sobreajuste. Nuestros resultados confirman esto:
+- Subconjunto de ajuste: **48% mejora** (5.83% CER)
+- Dataset completo: **0.8% mejora** (11.14% CER)
+
+La mejora mínima en el dataset completo indica que los hiperparámetros se sobreajustaron a las páginas 5-10. Los problemas de EasyOCR (detecciones espurias, pérdida de estructura) también pueden ser parcialmente a nivel de modelo.
+
+## Evaluación del Dataset Completo (45 páginas)
+
+| Métrica | Base | Ajustado | Mejora |
+|---------|------|----------|--------|
+| **CER** | 11.23% | 11.14% | **0.8%** |
+| **WER** | 36.36% | 36.85% | **-1.3%** |
+| Tiempo/Página | 1.84s | 1.94s | - |
+
+> **Nota:** El ajuste mostró mejora mínima en el dataset completo. Los problemas de EasyOCR pueden ser a nivel de modelo.
+
+## Resultados del Subconjunto de Ajuste (páginas 5-10)
+
+| Métrica | Base | Ajustado | Mejora |
+|---------|------|----------|--------|
+| **CER** | 11.23% | **5.83%** | **48%** |
+| **WER** | 36.36% | **26.33%** | **28%** |
+
+> Nota: Las grandes mejoras en el subconjunto de ajuste no se transfirieron al dataset completo.
+
+## Mejor Configuración Encontrada
+
+```json
+{
+ "text_threshold": 0.6647,
+ "low_text": 0.4247,
+ "link_threshold": 0.2184,
+ "slope_ths": 0.1629,
+ "ycenter_ths": 0.7994,
+ "height_ths": 0.6437,
+ "width_ths": 0.6065,
+ "add_margin": 0.1462,
+ "contrast_ths": 0.1671,
+ "adjust_contrast": 0.6416,
+ "decoder": "greedy",
+ "beamWidth": 7,
+ "min_size": 10
+}
+```
+
+## Hallazgos Clave
+
+1. **decoder: greedy** - Consistentemente mejor que beamsearch para este dataset
+2. **Mayor text_threshold (0.66)** - Reduce detecciones espurias
+3. **min_size: 10** - Filtra artefactos de ruido pequeños
+4. **Umbrales moderados** - Sensibilidad de detección balanceada
+
+## Impacto de Parámetros
+
+Parámetros que mejoraron la precisión:
+- `decoder="greedy"` consistentemente superó a beamsearch
+- Mayor `text_threshold` (0.6-0.8) redujo el ruido
+- `min_size >= 5` ayudó a filtrar artefactos
+
+Parámetros que perjudicaron la precisión:
+- `decoder="beamsearch"` causó ~35-40% CER en muchas pruebas
+- `text_threshold` muy bajo (<0.4) detectó demasiado ruido
+- `min_size` alto (>15) omitió algo de texto
+
+## Comparación con Problemas de Base
+
+Problemas originales identificados en el debugset:
+- Inserciones espurias de caracteres - **Mejorado** con umbrales más altos
+- Pérdida de estructura - Todavía presente pero menos severa
+
+## Evaluación del Dataset Completo
+
+**Estado:** Completado
+
+```bash
+curl -X POST http://localhost:8002/evaluate_full \
+ -H "Content-Type: application/json" \
+ -d '{
+ "pdf_folder": "/app/dataset",
+ "text_threshold": 0.6647,
+ "low_text": 0.4247,
+ "link_threshold": 0.2184,
+ "slope_ths": 0.1629,
+ "ycenter_ths": 0.7994,
+ "height_ths": 0.6437,
+ "width_ths": 0.6065,
+ "add_margin": 0.1462,
+ "contrast_ths": 0.1671,
+ "adjust_contrast": 0.6416,
+ "decoder": "greedy",
+ "beamWidth": 7,
+ "min_size": 10,
+ "save_output": true
+ }'
+```
+
+**Resultado:** CER 11.14%, WER 36.85%, 1.94s/página (mejora mínima)
+
+**Conclusión:** El ajuste de EasyOCR proporcionó mejora insignificante en el dataset completo.
diff --git a/docs/metrics/metrics_paddle.md b/docs/metrics/metrics_paddle.md
new file mode 100644
index 0000000..94b426d
--- /dev/null
+++ b/docs/metrics/metrics_paddle.md
@@ -0,0 +1,91 @@
+# Resultados de Ajuste de Hiperparámetros PaddleOCR
+
+**Fecha de Ajuste:** 2026-01-19
+**Plataforma:** NVIDIA RTX 3060 Laptop GPU
+**Muestras:** 64
+**Páginas de Prueba:** 5-10 (primer documento)
+
+### ¿Por Qué Solo 5 Páginas?
+
+Usamos solo 5 páginas (páginas 5-10) para el ajuste de hiperparámetros porque:
+
+1. **Velocidad**: 64 pruebas × 5 páginas = 320 evaluaciones de página. Con 45 páginas, serían 2,880 evaluaciones (~9x más tiempo)
+2. **Eficiencia de recursos**: Cada prueba toma ~3-10 segundos en GPU; el dataset completo tomaría ~1 hora por prueba en CPU
+
+**Riesgo de Sobreajuste**: El ajuste de hiperparámetros en un subconjunto pequeño PUEDE causar sobreajuste. Nuestros resultados confirman esto:
+- Subconjunto de ajuste: **90% mejora** (0.79% CER)
+- Dataset completo: **12.8% mejora** (7.72% CER)
+
+La diferencia dramática muestra que los hiperparámetros se sobreajustaron parcialmente a las páginas 5-10. Un subconjunto de ajuste más grande (ej. 15-20 páginas) podría producir parámetros que generalicen mejor, pero aumentaría el tiempo de ajuste proporcionalmente.
+
+## Evaluación del Dataset Completo (45 páginas)
+
+| Métrica | Base | Ajustado | Mejora |
+|---------|------|----------|--------|
+| **CER** | 8.85% | **7.72%** | **12.8%** |
+| **WER** | 13.05% | **11.40%** | **12.6%** |
+| Tiempo/Página | 0.51s | 0.55s | - |
+
+## Resultados del Subconjunto de Ajuste (páginas 5-10)
+
+| Métrica | Base | Ajustado | Mejora |
+|---------|------|----------|--------|
+| **CER** | 7.76% | **0.79%** | **90%** |
+| **WER** | 11.62% | **7.78%** | **33%** |
+
+> Nota: El subconjunto de ajuste mostró mayores mejoras, sugiriendo que algunos hiperparámetros son específicos de la página.
+
+## Mejor Configuración Encontrada
+
+```json
+{
+ "use_doc_orientation_classify": true,
+ "use_doc_unwarping": false,
+ "textline_orientation": true,
+ "text_det_thresh": 0.0462,
+ "text_det_box_thresh": 0.4862,
+ "text_det_unclip_ratio": 0.0,
+ "text_rec_score_thresh": 0.5658
+}
+```
+
+## Hallazgos Clave
+
+1. **textline_orientation: true** - Crítico para la precisión
+2. **use_doc_orientation_classify: true** - Ayuda con la detección de orientación de página
+3. **use_doc_unwarping: false** - El enderezamiento de documentos perjudica la precisión en este dataset
+4. **Bajo text_det_thresh (0.0462)** - Detección de texto más sensible ayuda
+5. **Mayor text_rec_score_thresh (0.5658)** - Filtra reconocimientos de baja confianza
+
+## Impacto de Parámetros
+
+Parámetros que mejoraron la precisión:
+- `textline_orientation=True` consistentemente en los mejores resultados
+- `use_doc_orientation_classify=True` en las mejores pruebas
+- Valores más bajos de `text_det_thresh` (0.04-0.10)
+
+Parámetros que perjudicaron la precisión:
+- `use_doc_unwarping=True` aumentó el CER significativamente
+- `text_det_box_thresh` muy bajo (<0.01) causó problemas
+
+## Evaluación del Dataset Completo
+
+**Estado:** Completado
+
+```bash
+curl -X POST http://localhost:8002/evaluate_full \
+ -H "Content-Type: application/json" \
+ -d '{
+ "pdf_folder": "/app/dataset",
+ "use_doc_orientation_classify": true,
+ "use_doc_unwarping": false,
+ "textline_orientation": true,
+ "text_det_thresh": 0.0462,
+ "text_det_box_thresh": 0.4862,
+ "text_det_unclip_ratio": 0.0,
+ "text_rec_score_thresh": 0.5658,
+ "save_output": true
+ }'
+```
+
+**Resultado:** CER 7.72%, WER 11.40%, 0.55s/página
diff --git a/src/paddle_ocr/docker-compose.gpu-registry.yml b/src/paddle_ocr/docker-compose.gpu-registry.yml
index bd9b991..43e6274 100644
--- a/src/paddle_ocr/docker-compose.gpu-registry.yml
+++ b/src/paddle_ocr/docker-compose.gpu-registry.yml
@@ -17,6 +17,8 @@ services:
environment:
- PYTHONUNBUFFERED=1
- CUDA_VISIBLE_DEVICES=0
+ - PADDLE_DET_MODEL=PP-OCRv5_mobile_det
+ - PADDLE_REC_MODEL=PP-OCRv5_mobile_rec
deploy:
resources:
reservations:
diff --git a/src/raytune_ocr.py b/src/raytune_ocr.py
index 1e3fddc..9d608a1 100644
--- a/src/raytune_ocr.py
+++ b/src/raytune_ocr.py
@@ -16,34 +16,57 @@ from ray import tune
from ray.tune.search.optuna import OptunaSearch
-def check_workers(ports: List[int], service_name: str = "OCR") -> List[str]:
+def check_workers(
+ ports: List[int],
+ service_name: str = "OCR",
+ timeout: int = 180,
+ interval: int = 5,
+) -> List[str]:
"""
- Verify workers are running and return healthy URLs.
+ Wait for workers to be fully ready (model + dataset loaded) and return healthy URLs.
Args:
ports: List of port numbers to check
service_name: Name for error messages
+ timeout: Max seconds to wait for each worker
+ interval: Seconds between retries
Returns:
List of healthy worker URLs
Raises:
- RuntimeError if no healthy workers found
+ RuntimeError if no healthy workers found after timeout
"""
+ import time
+
worker_urls = [f"http://localhost:{port}" for port in ports]
healthy_workers = []
for url in worker_urls:
- try:
- health = requests.get(f"{url}/health", timeout=10).json()
- if health.get('status') == 'ok' and health.get('model_loaded'):
- healthy_workers.append(url)
- gpu = health.get('gpu_name', 'CPU')
- print(f"✓ {url}: {health['status']} ({gpu})")
- else:
- print(f"✗ {url}: not ready yet")
- except requests.exceptions.ConnectionError:
- print(f"✗ {url}: not reachable")
+ print(f"Waiting for {url}...")
+ start = time.time()
+
+ while time.time() - start < timeout:
+ try:
+ health = requests.get(f"{url}/health", timeout=10).json()
+ model_ok = health.get('model_loaded', False)
+ dataset_ok = health.get('dataset_loaded', False)
+
+ if health.get('status') == 'ok' and model_ok:
+ gpu = health.get('gpu_name', 'CPU')
+ print(f"✓ {url}: ready ({gpu})")
+ healthy_workers.append(url)
+ break
+
+ elapsed = int(time.time() - start)
+ print(f" [{elapsed}s] model={model_ok}")
+ except requests.exceptions.RequestException:
+ elapsed = int(time.time() - start)
+ print(f" [{elapsed}s] not reachable")
+
+ time.sleep(interval)
+ else:
+ print(f"✗ {url}: timeout after {timeout}s")
if not healthy_workers:
raise RuntimeError(
@@ -51,7 +74,7 @@ def check_workers(ports: List[int], service_name: str = "OCR") -> List[str]:
f"Checked ports: {ports}"
)
- print(f"\n{len(healthy_workers)}/{len(worker_urls)} workers ready")
+ print(f"\n{len(healthy_workers)}/{len(worker_urls)} workers ready\n")
return healthy_workers
@@ -218,8 +241,8 @@ def correlation_analysis(df: pd.DataFrame, param_keys: List[str]) -> None:
# OCR-specific payload functions
# =============================================================================
-def paddle_ocr_payload(config: Dict, start_page: int = 5, end_page: int = 10, save_output: bool = False) -> Dict:
- """Create payload for PaddleOCR API."""
+def paddle_ocr_payload(config: Dict) -> Dict:
+ """Create payload for PaddleOCR API. Uses pages 5-10 (first doc) for tuning."""
return {
"pdf_folder": "/app/dataset",
"use_doc_orientation_classify": config.get("use_doc_orientation_classify", False),
@@ -229,14 +252,14 @@ def paddle_ocr_payload(config: Dict, start_page: int = 5, end_page: int = 10, sa
"text_det_box_thresh": config.get("text_det_box_thresh", 0.0),
"text_det_unclip_ratio": config.get("text_det_unclip_ratio", 1.5),
"text_rec_score_thresh": config.get("text_rec_score_thresh", 0.0),
- "start_page": start_page,
- "end_page": end_page,
- "save_output": save_output,
+ "start_page": 5,
+ "end_page": 10,
+ "save_output": False,
}
-def doctr_payload(config: Dict, start_page: int = 5, end_page: int = 10, save_output: bool = False) -> Dict:
- """Create payload for DocTR API."""
+def doctr_payload(config: Dict) -> Dict:
+ """Create payload for DocTR API. Uses pages 5-10 (first doc) for tuning."""
return {
"pdf_folder": "/app/dataset",
"assume_straight_pages": config.get("assume_straight_pages", True),
@@ -248,14 +271,14 @@ def doctr_payload(config: Dict, start_page: int = 5, end_page: int = 10, save_ou
"resolve_lines": config.get("resolve_lines", True),
"resolve_blocks": config.get("resolve_blocks", False),
"paragraph_break": config.get("paragraph_break", 0.035),
- "start_page": start_page,
- "end_page": end_page,
- "save_output": save_output,
+ "start_page": 5,
+ "end_page": 10,
+ "save_output": False,
}
-def easyocr_payload(config: Dict, start_page: int = 5, end_page: int = 10, save_output: bool = False) -> Dict:
- """Create payload for EasyOCR API."""
+def easyocr_payload(config: Dict) -> Dict:
+ """Create payload for EasyOCR API. Uses pages 5-10 (first doc) for tuning."""
return {
"pdf_folder": "/app/dataset",
"text_threshold": config.get("text_threshold", 0.7),
@@ -271,9 +294,9 @@ def easyocr_payload(config: Dict, start_page: int = 5, end_page: int = 10, save_
"decoder": config.get("decoder", "greedy"),
"beamWidth": config.get("beamWidth", 5),
"min_size": config.get("min_size", 10),
- "start_page": start_page,
- "end_page": end_page,
- "save_output": save_output,
+ "start_page": 5,
+ "end_page": 10,
+ "save_output": False,
}
diff --git a/src/results/raytune_doctr_results_20260119_121445.csv b/src/results/raytune_doctr_results_20260119_121445.csv
new file mode 100644
index 0000000..1f0ae1b
--- /dev/null
+++ b/src/results/raytune_doctr_results_20260119_121445.csv
@@ -0,0 +1,65 @@
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diff --git a/src/results/raytune_easyocr_results_20260119_120204.csv b/src/results/raytune_easyocr_results_20260119_120204.csv
new file mode 100644
index 0000000..0791ded
--- /dev/null
+++ b/src/results/raytune_easyocr_results_20260119_120204.csv
@@ -0,0 +1,65 @@
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diff --git a/src/results/raytune_paddle_results_20260119_122609.csv b/src/results/raytune_paddle_results_20260119_122609.csv
new file mode 100644
index 0000000..a1a8c89
--- /dev/null
+++ b/src/results/raytune_paddle_results_20260119_122609.csv
@@ -0,0 +1,65 @@
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diff --git a/src/run_tuning.py b/src/run_tuning.py
new file mode 100644
index 0000000..7b5a25f
--- /dev/null
+++ b/src/run_tuning.py
@@ -0,0 +1,74 @@
+#!/usr/bin/env python3
+"""Run hyperparameter tuning for OCR services."""
+
+import sys
+import argparse
+from raytune_ocr import (
+ check_workers, create_trainable, run_tuner, analyze_results,
+ paddle_ocr_payload, doctr_payload, easyocr_payload,
+ PADDLE_OCR_SEARCH_SPACE, DOCTR_SEARCH_SPACE, EASYOCR_SEARCH_SPACE,
+ PADDLE_OCR_CONFIG_KEYS, DOCTR_CONFIG_KEYS, EASYOCR_CONFIG_KEYS,
+)
+
+SERVICES = {
+ "paddle": {
+ "ports": [8002],
+ "payload_fn": paddle_ocr_payload,
+ "search_space": PADDLE_OCR_SEARCH_SPACE,
+ "config_keys": PADDLE_OCR_CONFIG_KEYS,
+ "name": "PaddleOCR",
+ },
+ "doctr": {
+ "ports": [8003],
+ "payload_fn": doctr_payload,
+ "search_space": DOCTR_SEARCH_SPACE,
+ "config_keys": DOCTR_CONFIG_KEYS,
+ "name": "DocTR",
+ },
+ "easyocr": {
+ "ports": [8002],
+ "payload_fn": easyocr_payload,
+ "search_space": EASYOCR_SEARCH_SPACE,
+ "config_keys": EASYOCR_CONFIG_KEYS,
+ "name": "EasyOCR",
+ },
+}
+
+def main():
+ parser = argparse.ArgumentParser(description="Run OCR hyperparameter tuning")
+ parser.add_argument("--service", choices=["paddle", "doctr", "easyocr"], required=True)
+ parser.add_argument("--samples", type=int, default=64, help="Number of samples")
+ args = parser.parse_args()
+
+ cfg = SERVICES[args.service]
+ print(f"\n{'='*50}")
+ print(f"Hyperparameter Tuning: {cfg['name']}")
+ print(f"Samples: {args.samples}")
+ print(f"{'='*50}\n")
+
+ # Check workers
+ healthy = check_workers(cfg["ports"], cfg["name"])
+
+ # Create trainable and run tuning
+ trainable = create_trainable(cfg["ports"], cfg["payload_fn"])
+ results = run_tuner(
+ trainable=trainable,
+ search_space=cfg["search_space"],
+ num_samples=args.samples,
+ num_workers=len(healthy),
+ )
+
+ # Analyze results
+ df = analyze_results(
+ results,
+ output_folder="results",
+ prefix=f"raytune_{args.service}",
+ config_keys=cfg["config_keys"],
+ )
+
+ print(f"\n{'='*50}")
+ print("Tuning complete!")
+ print(f"{'='*50}")
+
+if __name__ == "__main__":
+ main()