# Claude Code Context - Masters Thesis OCR Project

## Project Overview

This is a **Master's Thesis (TFM)** for UNIR's Master in Artificial Intelligence. The project focuses on **OCR hyperparameter optimization** using Ray Tune with Optuna for Spanish academic documents.

**Author:** Sergio Jiménez Jiménez
**University:** UNIR (Universidad Internacional de La Rioja)
**Year:** 2025

## Key Context

### Why Hyperparameter Optimization Instead of Fine-tuning

Due to **hardware limitations** (no dedicated GPU, CPU-only execution), the project pivoted from fine-tuning to hyperparameter optimization:
- Fine-tuning deep learning models without GPU is prohibitively slow
- Inference time is ~69 seconds/page on CPU
- Hyperparameter optimization proved to be an effective alternative, achieving 80.9% CER reduction

### Main Results

| Model | CER | Character Accuracy |
|-------|-----|-------------------|
| PaddleOCR Baseline | 7.78% | 92.22% |
| PaddleOCR-HyperAdjust | **1.49%** | **98.51%** |

**Goal achieved:** CER < 2% (target was < 2%, result is 1.49%)

### Optimal Configuration Found

```python
config_optimizada = {
    "textline_orientation": True,           # CRITICAL - reduces CER ~70%
    "use_doc_orientation_classify": False,
    "use_doc_unwarping": False,
    "text_det_thresh": 0.4690,
    "text_det_box_thresh": 0.5412,
    "text_det_unclip_ratio": 0.0,
    "text_rec_score_thresh": 0.6350,
}
```

### Key Findings

1. `textline_orientation=True` is the most impactful parameter (reduces CER by 69.7%)
2. `text_det_thresh` has -0.52 correlation with CER; values < 0.1 cause catastrophic failures
3. Document correction modules (`use_doc_orientation_classify`, `use_doc_unwarping`) are unnecessary for digital PDFs

## Repository Structure

```
MastersThesis/
├── docs/                    # Thesis chapters in Markdown
│   ├── 00_resumen.md        # Abstract (Spanish + English)
│   ├── 01_introduccion.md   # Introduction
│   ├── 02_contexto_estado_arte.md  # Context and State of Art
│   ├── 03_objetivos_metodologia.md # Objectives and Methodology
│   ├── 04_comparativa_soluciones.md # OCR Comparative Study
│   ├── 05_optimizacion_hiperparametros.md # Ray Tune Optimization
│   ├── 06_resultados_discusion.md  # Results and Discussion
│   └── 07_conclusiones_trabajo_futuro.md # Conclusions
├── src/
│   ├── paddle_ocr_fine_tune_unir_raytune.ipynb  # Main experiment (64 trials)
│   ├── paddle_ocr_tuning.py                      # CLI evaluation script
│   ├── dataset_manager.py                        # ImageTextDataset class
│   ├── prepare_dataset.ipynb                     # Dataset preparation
│   └── raytune_paddle_subproc_results_20251207_192320.csv  # 64 trial results
├── results/                 # Benchmark results CSVs
├── instructions/            # UNIR PDF document used as dataset
├── ocr_benchmark_notebook.ipynb  # Initial OCR benchmark
└── README.md
```

## Important Data Files

### Results CSV Files
- `src/raytune_paddle_subproc_results_20251207_192320.csv` - 64 Ray Tune trials with configs and metrics
- `results/ai_ocr_benchmark_finetune_results_20251206_113206.csv` - Per-page OCR benchmark results

### Key Notebooks
- `src/paddle_ocr_fine_tune_unir_raytune.ipynb` - Main Ray Tune experiment
- `src/prepare_dataset.ipynb` - PDF to image/text conversion
- `ocr_benchmark_notebook.ipynb` - EasyOCR vs PaddleOCR vs DocTR comparison

## Technical Stack

| Component | Version |
|-----------|---------|
| Python | 3.11.9 |
| PaddlePaddle | 3.2.2 |
| PaddleOCR | 3.3.2 |
| Ray | 2.52.1 |
| Optuna | 4.6.0 |

## Pending Work

### Priority Tasks
1. **Validate on other document types** - Test optimal config on invoices, forms, contracts
2. **Expand dataset** - Current dataset has only 24 pages
3. **Complete unified thesis document** - Merge docs/ chapters into final UNIR format
4. **Create presentation slides** - For thesis defense

### Optional Extensions
- Explore `text_det_unclip_ratio` parameter (was fixed at 0.0)
- Compare with actual fine-tuning (if GPU access obtained)
- Multi-objective optimization (CER + WER + inference time)

## Guidelines for Claude

### When Working on This Project

1. **Be rigorous with data**: Only cite numbers from actual CSV files and notebook outputs. Do not fabricate comparison data.

2. **Reference sources**: When discussing results, reference the specific files:
   - Ray Tune results: `src/raytune_paddle_subproc_results_20251207_192320.csv`
   - Benchmark results: `results/ai_ocr_benchmark_finetune_results_20251206_113206.csv`

3. **Key files to read first**:
   - This file (`claude.md`) for context
   - `README.md` for current project state
   - Relevant `docs/` chapter for specific topics

4. **Language**: Documentation is in Spanish (thesis requirement), code comments in English.

5. **Hardware context**: Remember this is CPU-only execution. Any suggestions about GPU training or real-time processing should acknowledge this limitation.

### Common Tasks

- **Adding new experiments**: Update `src/paddle_ocr_fine_tune_unir_raytune.ipynb`
- **Updating documentation**: Edit files in `docs/`
- **Dataset expansion**: Use `src/prepare_dataset.ipynb` as template
- **Running evaluations**: Use `src/paddle_ocr_tuning.py` CLI

## Experiment Details

### Ray Tune Configuration
```python
tuner = tune.Tuner(
    trainable_paddle_ocr,
    tune_config=tune.TuneConfig(
        metric="CER",
        mode="min",
        search_alg=OptunaSearch(),
        num_samples=64,
        max_concurrent_trials=2
    )
)
```

### Dataset
- Source: UNIR TFE instructions PDF
- Pages: 24
- Resolution: 300 DPI
- Ground truth: Extracted via PyMuPDF

### Metrics
- CER (Character Error Rate) - Primary metric
- WER (Word Error Rate) - Secondary metric
- Calculated using `jiwer` library