Update ReadME.md

README.md

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----
-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
----
+---
+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.