README.md

---
license: mit
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: val
    path: data/val-*
  - split: test
    path: data/test-*
dataset_info:
  features:
  - name: id
    dtype: int64
  - name: image_id
    dtype: string
  - name: image
    dtype: image
  - name: text
    dtype: string
  - name: caption
    dtype: string
  - name: prompt
    dtype: string
  - name: split
    dtype: string
  - name: ocr_confidence
    dtype: float64
  - name: ocr_backend
    dtype: string
  - name: caption_model
    dtype: string
  - name: source
    dtype: string
  - name: sharpness
    dtype: float64
  - name: brightness
    dtype: float64
  - name: contrast
    dtype: float64
  - name: resolution_w
    dtype: int64
  - name: resolution_h
    dtype: int64
  - name: text_length
    dtype: int64
  - name: word_count
    dtype: int64
  - name: phrase_reconstructed
    dtype: bool
  splits:
  - name: train
    num_bytes: 58573006
    num_examples: 800
  - name: val
    num_bytes: 6821157
    num_examples: 100
  - name: test
    num_bytes: 6848431
    num_examples: 100
  download_size: 72132017
  dataset_size: 72242594
task_categories:
  - image-to-text
  - text-to-image
language:
  - en
tags:
  - ocr
  - image-captioning
  - text-rendering
  - synthetic
  - blip2
  - easyocr
  - flux
size_categories:
  - 1K<n<10K
source_datasets:
  - stzhao/AnyWord-3M
---

# Text-in-Image OCR Dataset

*Built for **Project 12 — Efficient Image Generation**, as part of the ENSTA course [CSC_5IA21](https://giannifranchi.github.io/CSC_5IA21.html)*

**Team:** Adam Gassem · Asma Walha · Achraf Chaouch · Takoua Ben Aissa · Amaury Lorin  
**Tutors:** Arturo Mendoza Quispe · Nacim Belkhir

---

## Dataset Summary

A curated text-in-image dataset designed for fine-tuning text-to-image generative models (e.g. FLUX, Stable Diffusion, ControlNet) on accurate **text rendering**. Each sample pairs a real-world image containing readable text with:

- a verified OCR transcription (EasyOCR),
- a visual caption (BLIP-2),
- and a training prompt that embeds the OCR text verbatim.

Images are sourced from [AnyWord-3M](https://huggingface.co/datasets/stzhao/AnyWord-3M) and pass a rigorous multi-step quality pipeline before inclusion.

---

## Dataset Structure

| Split | Size |
|-------|------|
| train | 800 samples |
| val   | 100 samples |
| test  | 100 samples |

### Fields

| Field | Type | Description |
|-------|------|-------------|
| `image` | Image | The filtered image (512 px, JPEG) |
| `text` | string | Verified OCR text found in the image |
| `caption` | string | General visual description generated by BLIP-2 |
| `prompt` | string | Training prompt embedding the OCR text verbatim |
| `ocr_confidence` | float | EasyOCR confidence score (0–100) |
| `ocr_backend` | string | OCR engine used (`easyocr`) |
| `caption_model` | string | Captioning model used (`blip2` or `blip`) |
| `source` | string | AnyWord-3M subset of origin |
| `sharpness` | float | Laplacian variance of the image |
| `brightness` | float | Mean pixel brightness |
| `contrast` | float | Pixel standard deviation |
| `resolution_w` / `resolution_h` | int | Image dimensions in pixels |
| `text_length` | int | Character count of the OCR text |
| `word_count` | int | Word count of the OCR text |
| `phrase_reconstructed` | bool | Whether the full phrase was expanded beyond the bounding box |

### Sample record

```json
{
  "image": "<PIL.Image>",
  "text": "OPEN",
  "caption": "A storefront with a neon sign above the door.",
  "prompt": "A storefront with a neon sign above the door, with the text \"OPEN\" clearly visible",
  "ocr_confidence": 87.5,
  "source": "AnyWord-3M/laion",
  "sharpness": 142.3,
  "resolution_w": 512,
  "resolution_h": 384
}
```

---

## Usage

```python
from datasets import load_dataset

ds = load_dataset("your-org/your-dataset-name")

# Access a training sample
sample = ds["train"][0]
print(sample["prompt"])
sample["image"].show()
```

For fine-tuning with the prompt field:

```python
for sample in ds["train"]:
    image  = sample["image"]      # PIL image
    prompt = sample["prompt"]     # text-conditioned training caption
    text   = sample["text"]       # ground-truth OCR string
```

---

## Creation Pipeline

Images are drawn from AnyWord-3M (streamed) and pass through the following stages:

```
AnyWord-3M stream
      │
      ▼
1. Annotation filtering   → valid, short, English text regions only
      │
      ▼
2. Image quality gate     → resolution ≥ 256 px, sharpness ≥ 80,
                            brightness 30–230, contrast ≥ 20
      │
      ▼
3. EasyOCR verify         → confirm annotated text is readable (conf ≥ 0.40)
      │
      ▼
4. EasyOCR reconstruct    → expand to the full visible phrase
      │
      ▼
5. BLIP-2 caption         → general visual description
      │
      ▼
6. Prompt construction    → natural sentence with OCR text in quotes
      │
      ▼
7. Split & save           → 80 % train / 10 % val / 10 % test
```

---

## Source Subsets

| Subset | Description |
|--------|-------------|
| `laion` | Web-crawled natural images |
| `OCR_COCO_Text` | COCO scene text |
| `OCR_mlt2019` | Multi-language (English filtered) |
| `OCR_Art` | Artistic / designed text |

---

## Citation & Project

This dataset was produced as part of the **Efficient Image Generation** project at ENSTA Paris.  
Full methodology, training experiments, and inference benchmarks are documented in the [project report](https://drive.google.com/file/d/1ay4-cBOSt4LbLhwgQ0gBykda1Bu0HUXY/view?usp=drive_link).

---

## License

Released under the **MIT License** — free to use, modify, and distribute without restriction. Note that the AnyWord-3M source dataset and BLIP-2 model are subject to their own respective licenses on HuggingFace.