# 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

The project chose **hyperparameter optimization** over fine-tuning because:
- Fine-tuning requires extensive labeled datasets specific to the domain
- Hyperparameter tuning can improve pretrained models without retraining
- GPU acceleration (RTX 3060) enables efficient exploration of hyperparameter space

### Main Results (GPU - Jan 2026)

| Model | CER | Character Accuracy |
|-------|-----|-------------------|
| PaddleOCR Baseline | 8.85% | 91.15% |
| PaddleOCR-HyperAdjust (full dataset) | **7.72%** | **92.28%** |
| PaddleOCR-HyperAdjust (best trial) | **0.79%** | **99.21%** |

**Goal status:** CER < 2% achieved in best trial (0.79%). Full dataset shows 12.8% improvement.

### Optimal Configuration Found (GPU)

```python
config_optimizada = {
    "textline_orientation": True,           # CRITICAL for complex layouts
    "use_doc_orientation_classify": True,   # Improves document orientation
    "use_doc_unwarping": False,
    "text_det_thresh": 0.0462,              # -0.52 correlation with CER
    "text_det_box_thresh": 0.4862,
    "text_det_unclip_ratio": 0.0,
    "text_rec_score_thresh": 0.5658,
}
```

### Key Findings

1. `textline_orientation=True` is critical for documents with mixed layouts
2. `use_doc_orientation_classify=True` improves document orientation detection in GPU config
3. `text_det_thresh` has -0.52 correlation with CER; values < 0.01 cause catastrophic failures
4. `use_doc_unwarping=False` is optimal for digital PDFs (unnecessary processing)

## Repository Structure

```
MastersThesis/
├── docs/                    # Thesis chapters in Markdown (UNIR template structure)
│   ├── 00_resumen.md                      # Resumen + Abstract + Keywords
│   ├── 01_introduccion.md                 # 1. Introducción (1.1, 1.2, 1.3)
│   ├── 02_contexto_estado_arte.md         # 2. Contexto y estado del arte (2.1, 2.2, 2.3)
│   ├── 03_objetivos_metodologia.md        # 3. Objetivos y metodología (3.1, 3.2, 3.3, 3.4)
│   ├── 04_desarrollo_especifico.md        # 4. Desarrollo específico (4.1, 4.2, 4.3)
│   ├── 05_conclusiones_trabajo_futuro.md  # 5. Conclusiones (5.1, 5.2)
│   ├── 06_referencias_bibliograficas.md   # Referencias bibliográficas (APA format)
│   └── 07_anexo_a.md                      # Anexo A: Código fuente y datos
├── thesis_output/           # Generated thesis document
│   ├── plantilla_individual.htm           # Complete TFM (open in Word)
│   └── figures/                           # PNG figures from Mermaid diagrams
│       ├── figura_1.png ... figura_7.png
│       └── figures_manifest.json
├── 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 instructions and template
│   ├── instrucciones.pdf         # TFE writing guidelines
│   ├── plantilla_individual.pdf  # Word template (PDF version)
│   └── plantilla_individual.htm  # Word template (HTML version, source)
├── apply_content.py         # Main orchestrator: generates TFM from docs/ + template
├── content_handlers.py      # Content block handlers (tables, figures, lists, etc.)
├── markdown_utils.py        # Markdown utilities (cross-refs, latex, metadata extraction)
├── generate_mermaid_figures.py  # Converts Mermaid diagrams to PNG
├── src/archived/ocr_benchmark_notebook.ipynb  # Archived benchmark (do not cite)
└── README.md
```

### docs/ to Template Mapping

The template (`plantilla_individual.pdf`) requires **5 chapters**. The docs/ files now match this structure exactly:

| Template Section | docs/ File | Notes |
|-----------------|------------|-------|
| Resumen | `00_resumen.md` (Spanish part) | 150-300 words + Palabras clave |
| Abstract | `00_resumen.md` (English part) | 150-300 words + Keywords |
| 1. Introducción | `01_introduccion.md` | Subsections 1.1, 1.2, 1.3 |
| 2. Contexto y estado del arte | `02_contexto_estado_arte.md` | Subsections 2.1, 2.2, 2.3 + Mermaid diagrams |
| 3. Objetivos y metodología | `03_objetivos_metodologia.md` | Subsections 3.1, 3.2, 3.3, 3.4 + Mermaid diagrams |
| 4. Desarrollo específico | `04_desarrollo_especifico.md` | Subsections 4.1, 4.2, 4.3 + Mermaid charts |
| 5. Conclusiones y trabajo futuro | `05_conclusiones_trabajo_futuro.md` | Subsections 5.1, 5.2 |
| Referencias bibliográficas | `06_referencias_bibliograficas.md` | APA, alphabetical |
| Anexo A | `07_anexo_a.md` | Repository URL + structure |

## Important Data Files

### Results CSV Files (GPU - PRIMARY)
- `src/results/raytune_paddle_results_20260119_122609.csv` - 64 Ray Tune trials PaddleOCR GPU (PRIMARY)
- `src/results/raytune_easyocr_results_20260119_120204.csv` - 64 Ray Tune trials EasyOCR GPU
- `src/results/raytune_doctr_results_20260119_121445.csv` - 64 Ray Tune trials DocTR GPU

### Results CSV Files (CPU - time reference only)
- `src/raytune_paddle_subproc_results_20251207_192320.csv` - CPU execution for time comparison (69.4s/page vs 0.84s/page GPU)

### Key Scripts
- `src/run_tuning.py` - Main Ray Tune optimization script
- `src/raytune/raytune_ocr.py` - Ray Tune utilities and search spaces
- `src/paddle_ocr/paddle_ocr_tuning_rest.py` - PaddleOCR REST API

## 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

### Completed Tasks
- [x] **Structure docs/ to match UNIR template** - All chapters now follow exact numbering (1.1, 1.2, etc.)
- [x] **Add Mermaid diagrams** - 7 diagrams added (OCR pipeline, Ray Tune architecture, methodology flowcharts, CER comparison charts)
- [x] **Generate unified thesis document** - `apply_content.py` generates complete document from docs/
- [x] **Convert Mermaid to PNG** - `generate_mermaid_figures.py` generates figures automatically
- [x] **Proper template formatting** - Tables/figures use `Piedefoto-tabla` class, references use `MsoBibliography`

### Priority Tasks
1. **Validate on other document types** - Test optimal config on invoices, forms, contracts
2. **Use larger tuning subset** - Current 5 pages caused overfitting; recommend 15-20 pages
3. **Create presentation slides** - For thesis defense
4. **Final document review** - Open in Word, update indices (Ctrl+A, F9), verify formatting

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

## Thesis Document Generation

To regenerate the thesis document:

```bash
# 1. Generate PNG figures from Mermaid diagrams
python3 generate_mermaid_figures.py

# 2. Apply docs/ content to UNIR template
python3 apply_content.py

# 3. Open in Word and finalize
# - Open thesis_output/plantilla_individual.htm in Microsoft Word
# - Press Ctrl+A then F9 to update all indices
# - Save as .docx
```

**What `apply_content.py` does:**

The script is organized into three modules for maintainability:
- `apply_content.py` - Main orchestrator (~300 lines)
- `content_handlers.py` - Block-level content handlers (~400 lines)
- `markdown_utils.py` - Utility functions (~150 lines)

Functionality:
- Replaces Resumen and Abstract with actual content + keywords
- Replaces all 5 chapters with content from docs/
- Replaces Referencias with APA-formatted bibliography
- Replaces Anexo with repository information
- Converts Mermaid diagrams to embedded PNG images
- Formats tables with `Piedefoto-tabla` captions and sources
- Removes template instruction text ("Importante:", "Ejemplo de nota al pie", etc.)

---

## UNIR TFE Document Guidelines

**CRITICAL:** The thesis MUST follow UNIR's official template (`instructions/plantilla_individual.pdf`) and guidelines (`instructions/instrucciones.pdf`).

### Work Type Classification

This thesis is a **hybrid of Type 1 (Piloto experimental) and Type 3 (Comparativa de soluciones)**:
- Comparative study of OCR solutions (EasyOCR, PaddleOCR, DocTR)
- Experimental pilot with Ray Tune hyperparameter optimization
- 64 trials executed, results analyzed statistically

### Document Structure (from plantilla_individual.pdf - MANDATORY)

The TFE must follow this EXACT structure from the official template:

| Section | Subsections | Notes |
|---------|-------------|-------|
| **Portada** | Title, Author, Type, Director, Date | Use template format exactly |
| **Resumen** | 150-300 words + 3-5 Palabras clave | Spanish summary |
| **Abstract** | 150-300 words + 3-5 Keywords | English summary |
| **Índice de contenidos** | Auto-generated | New page |
| **Índice de figuras** | Auto-generated | New page |
| **Índice de tablas** | Auto-generated | New page |
| **1. Introducción** | 1.1 Motivación, 1.2 Planteamiento del trabajo, 1.3 Estructura del trabajo | 3-5 pages |
| **2. Contexto y estado del arte** | 2.1 Contexto del problema, 2.2 Estado del arte, 2.3 Conclusiones | 10-15 pages |
| **3. Objetivos concretos y metodología** | 3.1 Objetivo general, 3.2 Objetivos específicos, 3.3 Metodología del trabajo | Variable |
| **4. Desarrollo específico** | Varies by work type (see below) | Main content |
| **5. Conclusiones y trabajo futuro** | 5.1 Conclusiones, 5.2 Líneas de trabajo futuro | Variable |
| **Referencias bibliográficas** | APA format, alphabetical, hanging indent | Variable |
| **Anexo A** | Código fuente y datos analizados | Repository URL |

**Total length:** 50-90 pages (excluding cover, resumen, abstract, indices, annexes)

### Chapter-Specific Requirements (from plantilla_individual.pdf)

#### 1. Introducción
The introduction must give a clear first idea of what was intended, the conclusions reached, and the procedure followed. Key ideas: problem identification, justification of importance, general objectives, preview of contribution.

**1.1 Motivación:**
- Present the problem to solve
- Justify importance to educational/scientific community
- Answer: What problem? What are the causes? Why is it relevant?
- Must include references to prior research

**1.2 Planteamiento del trabajo:**
- Briefly state the problem/need detected
- Describe the proposal and purpose
- Answer: How to solve? What is proposed?

**1.3 Estructura del trabajo:**
- Briefly describe what each subsequent chapter contains

#### 2. Contexto y estado del arte
Study the application domain in depth, citing numerous references. Must consult different sources (not just online - also technical manuals, books).

**2.1 Contexto del problema:**
- Deep study of the application domain

**2.2 Estado del arte:**
- Antecedents, current studies, comparison of existing tools
- Must reference key authors in the field (justify exclusions)

**2.3 Conclusiones:**
- Summary linking research to the work to be done
- How findings affect the specific development

#### 3. Objetivos concretos y metodología de trabajo
Bridge between domain study and contribution. Three required elements: (1) general objective, (2) specific objectives, (3) methodology.

**3.1 Objetivo general:**
- Must be SMART (Doran, 1981)
- Focus on achieving an observable effect, not just "create a tool"
- Example: "Mejorar el servicio X logrando Y valorado positivamente (mínimo 4/5) por Z"

**3.2 Objetivos específicos:**
- Divide general objective into analyzable sub-objectives
- Must be SMART
- Use infinitive verbs: Analizar, Calcular, Clasificar, Comparar, Conocer, Cuantificar, Desarrollar, Describir, Descubrir, Determinar, Establecer, Explorar, Identificar, Indagar, Medir, Sintetizar, Verificar
- Typically ~5 objectives: 1-2 about state of art, 2-3 about development

**3.3 Metodología del trabajo:**
- Describe steps to achieve objectives
- Explain WHY each step
- What instruments will be used
- How results will be analyzed

#### 4. Desarrollo específico de la contribución
Structure depends on work type. Organize by methodology phases/activities.

**For Type 1 (Piloto experimental):**
- 4.1 Descripción detallada del experimento
  - Technologies used (with justification)
  - How pilot was organized
  - Participants (demographics)
  - Automatic evaluation techniques
  - How experiment proceeded
  - Monitoring/evaluation instruments
  - Statistical analysis types
- 4.2 Descripción de los resultados (objective, no interpretation)
  - Summary tables, result graphs, relevant data identification
- 4.3 Discusión
  - Relevance of results, explanations for anomalies, highlight key findings

**For Type 3 (Comparativa de soluciones):**
- 4.1 Planteamiento de la comparativa
  - Problem identification, alternative solutions to evaluate
  - Success criteria, measures to take
- 4.2 Desarrollo de la comparativa
  - All results and measurements obtained
  - Graphs, tables, data visualization
- 4.3 Discusión y análisis de resultados
  - Discussion of meaning, advantages/disadvantages of solutions

#### 5. Conclusiones y trabajo futuro

**5.1 Conclusiones:**
- Summary of problem, approach, and why solution is valid
- Summary of contributions
- **Relate contributions and results to objectives** - discuss degree of achievement

**5.2 Líneas de trabajo futuro:**
- Future work that would add value
- Justify how contribution can be used and in what fields

### SMART Objectives Requirements

ALL objectives (general and specific) MUST be SMART:

| Criterion | Requirement | Example from this thesis |
|-----------|-------------|-------------------------|
| **S**pecific | Clearly define what to achieve | "Optimizar PaddleOCR para documentos en español" |
| **M**easurable | Quantifiable success metric | "CER < 2%" |
| **A**ttainable | Feasible with available resources | "Sin GPU, usando optimización de hiperparámetros" |
| **R**elevant | Demonstrable impact | "Mejora extracción de texto en documentos académicos" |
| **T**ime-bound | Achievable in timeframe | "Un cuatrimestre" |

### Citation and Reference Rules

#### APA Format is MANDATORY

Reference guide: [https://bibliografiaycitas.unir.net/](https://bibliografiaycitas.unir.net/)

**In-text citations:**
- Single author: (Du, 2020) or Du (2020)
- Two authors: (Du & Li, 2020)
- Three+ authors: (Du et al., 2020)

**Reference list examples:**
```
# Journal article with DOI
Shi, B., Bai, X., & Yao, C. (2016). An end-to-end trainable neural network
  for image-based sequence recognition. IEEE Transactions on Pattern
  Analysis and Machine Intelligence, 39(11), 2298-2304.
  https://doi.org/10.1109/TPAMI.2016.2646371

# Conference paper
Akiba, T., Sano, S., Yanase, T., Ohta, T., & Koyama, M. (2019). Optuna:
  A next-generation hyperparameter optimization framework. Proceedings
  of the 25th ACM SIGKDD, 2623-2631.
  https://doi.org/10.1145/3292500.3330701

# arXiv preprint
Du, Y., Li, C., Guo, R., ... & Wang, H. (2020). PP-OCR: A practical ultra
  lightweight OCR system. arXiv preprint arXiv:2009.09941.
  https://arxiv.org/abs/2009.09941

# Software/GitHub repository
PaddlePaddle. (2024). PaddleOCR: Awesome multilingual OCR toolkits based
  on PaddlePaddle. GitHub. https://github.com/PaddlePaddle/PaddleOCR

# Book
Cohen, J. (1988). Statistical power analysis for the behavioral sciences
  (2nd ed.). Lawrence Erlbaum Associates.
```

#### Reference Rules
- **NO Wikipedia citations**
- Include variety: books, conferences, journal articles (not just URLs)
- All cited references must appear in reference list
- All references in list must be cited in text
- Order alphabetically by first author's surname
- Include DOI or URL when available

### Document Formatting Rules

#### Page Setup
| Element | Specification |
|---------|--------------|
| Page size | A4 |
| Left margin | 3.0 cm |
| Right margin | 2.0 cm |
| Top/Bottom margins | 2.5 cm |
| Header | Student name + TFE title |
| Footer | Page number |

#### Typography
| Element | Format |
|---------|--------|
| Body text | Calibri 12, justified, 1.5 line spacing, 6pt before/after |
| Título 1 | Calibri Light 18, blue, justified, 1.5 spacing |
| Título 2 | Calibri Light 14, blue, justified, 1.5 spacing |
| Título 3 | Calibri Light 12, justified, 1.5 spacing |
| Footnotes | Calibri 10, justified, single spacing |
| Code | Can reduce to 9pt if needed |

#### Tables and Figures (from plantilla_individual.pdf)

**Table format example:**
```
Tabla 1. Ejemplo de tabla con sus principales elementos.
[TABLE CONTENT]
Fuente: American Psychological Association, 2020a.
```

**Figure format example:**
```
Figura 1. Ejemplo de figura realizada para nuestro trabajo.
[FIGURE]
Fuente: American Psychological Association, 2020b.
```

**Rules:**
- **Title position**: Above the table/figure
- **Numbering format**: "**Tabla 1.**" / "**Figura 1.**" (Calibri 12, bold)
- **Title text**: Calibri 12, italic (after the number)
- **Source**: Below, centered, format "Fuente: Author, Year."
- Can reduce font to 9pt for dense tables
- Can use landscape orientation for large tables
- Tables should have horizontal lines only (no vertical lines) per APA style

### Writing Style Rules

#### MUST DO:
- Each chapter starts with introductory paragraph explaining content
- Each paragraph has at least 3 sentences
- Verify originality (cite all sources)
- Check spelling with Word corrector
- Ensure logical flow between paragraphs
- Define concepts and include pertinent citations

#### MUST NOT DO:
- Two consecutive headings without text between them
- Superfluous phrases and repetition of ideas
- Short paragraphs (less than 3 sentences)
- Missing figure/table numbers or titles
- Broken index generation

### Annexes Requirements

**Anexo A - Código fuente y datos:**
- Include repository URL where code is hosted
- Student must be sole author and owner of repository
- No commits from other users
- Data used should also be in repository
- If confidential (company project), justify why not shared

### Final Submission

- **Drafts**: Submit in Word format
- **Final deposit**: Submit in PDF format
- Verify all indices generate correctly before final submission

---

## Guidelines for Claude

### CRITICAL: Academic Rigor Requirements

**This is a Master's Thesis. Academic rigor is NON-NEGOTIABLE.**

#### DO NOT:
- **NEVER fabricate data or statistics** - Every number must come from an actual file in this repository
- **NEVER invent comparison results** - If we don't have data for EasyOCR or DocTR comparisons, don't make up numbers
- **NEVER assume or estimate values** - If a metric isn't in the CSV/notebook, don't include it
- **NEVER extrapolate beyond what the data shows** - 24 pages is a limited dataset, acknowledge this
- **NEVER claim results that weren't measured** - Only report what was actually computed

#### ALWAYS:
- **Read the source file first** before citing any result
- **Quote exact values** from CSV files (e.g., CER 0.011535 not "approximately 1%")
- **Reference the specific file and location** for every data point
- **Acknowledge limitations** explicitly (dataset size, CPU-only, single document type)
- **Distinguish between measured results and interpretations**

#### Data Sources (ONLY use these):
| Data Type | Source File |
|-----------|-------------|
| Ray Tune 64 trials | `src/raytune_paddle_subproc_results_20251207_192320.csv` |
| Experiment code | `src/paddle_ocr_fine_tune_unir_raytune.ipynb` |
| Final comparison | Output cells in the notebook (baseline vs optimized) |

#### Example of WRONG vs RIGHT:

**WRONG:** "EasyOCR achieved 8.5% CER while PaddleOCR achieved 5.2% CER"
(We don't have this comparison data in our results files)

**RIGHT:** "The optimization reduced CER from 7.78% to 1.49%, a reduction of 80.9% (source: final comparison in `paddle_ocr_fine_tune_unir_raytune.ipynb`)"

**WRONG:** "The optimization improved results by approximately 80%"

**RIGHT:** "From the 64 trials in `raytune_paddle_subproc_results_20251207_192320.csv`, minimum CER achieved was 1.15%"

### When Working on Documentation

1. **Read UNIR guidelines first**: Check `instructions/instrucciones.pdf` for structure requirements

2. **Follow chapter structure**: Each chapter has specific content requirements per UNIR guidelines

3. **References are UNIFIED**: All references go in `docs/06_referencias_bibliograficas.md`, NOT per-chapter

4. **Use APA format**: All citations must follow APA style

5. **Include "Fuentes de datos"**: Each chapter should list which repository files the data came from

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

7. **Hardware context**: Remember this is CPU-only execution. Any suggestions about GPU training should acknowledge this limitation

8. **When in doubt, ask**: If the user requests data that doesn't exist, ask rather than inventing numbers

9. **DIAGRAMS MUST BE IN MERMAID FORMAT**: All diagrams, flowcharts, and visualizations in the documentation MUST use Mermaid syntax. This ensures:
   - Version control friendly (text-based)
   - Consistent styling across all chapters
   - Easy to edit and maintain
   - Renders properly in GitHub and most Markdown viewers

   **Supported Mermaid diagram types:**
   - `flowchart` / `graph` - For pipelines, workflows, architectures
   - `xychart-beta` - For bar charts, comparisons
   - `sequenceDiagram` - For process interactions
   - `classDiagram` - For class structures
   - `stateDiagram` - For state machines
   - `pie` - For proportional data

   **Example:**
   ```mermaid
   flowchart LR
       A[Input] --> B[Process] --> C[Output]
   ```

### Common Tasks

- **Adding new experiments**: Update `src/paddle_ocr_fine_tune_unir_raytune.ipynb`
- **Updating documentation**: Edit files in `docs/`
- **Adding references**: Add to `docs/06_referencias_bibliograficas.md` (unified list)
- **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
