| --- |
| license: cc-by-4.0 |
| task_categories: |
| - image-to-text |
| language: |
| - zh |
| - en |
| tags: |
| - ocr |
| - vision |
| - multimodal |
| - document-understanding |
| - image-text-to-text |
| size_categories: |
| - 10K<n<100K |
| pretty_name: TextRenderLab OCR Dataset |
| --- |
| |
| # TextRenderLab OCR Dataset |
|
|
| This dataset is a synthetic OCR instruction-tuning dataset built with TextRenderLab. It contains rendered text images paired with OCR prompts and full transcription targets in both Traditional Chinese and English. |
|
|
| ## Dataset Summary |
|
|
| - **Task**: image-to-text OCR transcription |
| - **Languages**: Traditional Chinese, English |
| - **Total samples**: 40,000 |
| - **Language split**: |
| - Chinese: 20,000 |
| - English: 20,000 |
| - **Image format**: PNG |
| - **Annotation format**: JSONL |
|
|
| Each sample contains: |
|
|
| 1. A rendered image path under `images/` |
| 2. An OCR-style user question in Chinese |
| 3. A full text transcription as the answer |
| 4. Chat-style fields for multimodal training pipelines |
|
|
| ## Data Structure |
|
|
| Each line in `dataset.jsonl` is a JSON object with the following fields: |
|
|
| | Field | Type | Description | |
| |---|---|---| |
| | `unique_id` | `string` | Unique sample identifier | |
| | `images` | `list[string]` | Relative image path list, typically one PNG file | |
| | `question` | `string` | OCR prompt | |
| | `answer` | `string` | Full transcription target | |
| | `messages` | `list[object]` | Chat-style training format with `<image>` prompt | |
| | `axolotl_messages` | `list[object]` | Axolotl-compatible multimodal message format | |
|
|
| ## Example |
|
|
| ```json |
| { |
| "unique_id": "0ff0e0d5-afcf-48aa-a08b-c6d1a1ddc5eb", |
| "images": ["images/en_ocr_00000.png"], |
| "question": "圖片裡有些字，請幫我轉錄出來。", |
| "answer": "Under the legal framework that defined Hong Kong’s semi-autonomy after its return to China in 1997, many in the territory assumed that it would mostly manage its own affairs for decades.", |
| "messages": [ |
| {"role": "user", "content": "<image>圖片裡有些字，請幫我轉錄出來。"}, |
| {"role": "assistant", "content": "Under the legal framework that defined Hong Kong’s semi-autonomy after its return to China in 1997, many in the territory assumed that it would mostly manage its own affairs for decades."} |
| ], |
| "axolotl_messages": [ |
| { |
| "role": "user", |
| "content": [ |
| {"type": "image", "url": "images/en_ocr_00000.png"}, |
| {"type": "text", "text": "圖片裡有些字，請幫我轉錄出來。"} |
| ] |
| }, |
| { |
| "role": "assistant", |
| "content": [ |
| {"type": "text", "text": "Under the legal framework that defined Hong Kong’s semi-autonomy after its return to China in 1997, many in the territory assumed that it would mostly manage its own affairs for decades."} |
| ] |
| } |
| ] |
| } |
| ``` |
|
|
| ## Intended Use |
|
|
| This dataset is intended for: |
|
|
| - OCR fine-tuning for multimodal LLMs |
| - Image-text transcription training |
| - Bilingual OCR robustness experiments |
| - Synthetic pretraining or instruction-tuning pipelines |
|
|
| ## Notes |
|
|
| - The images are synthetically rendered rather than photographed. |
| - Prompts are phrased in Chinese, while answers may be either Chinese or English depending on the source text. |
| - Relative image paths in `images` assume the repository or dataset root contains both `dataset.jsonl` and the `images/` directory. |
|
|
| ## Loading Example |
|
|
| ```python |
| import json |
| from pathlib import Path |
| |
| dataset_path = Path("dataset.jsonl") |
| with dataset_path.open("r", encoding="utf-8") as f: |
| first = json.loads(next(f)) |
| |
| print(first["images"]) |
| print(first["question"]) |
| print(first["answer"][:200]) |
| ``` |
|
|
| ## Citation |
|
|
| If you use this dataset, please cite or reference the Hugging Face dataset repository: |
|
|
| `minyichen/Render-OCR` |
|
|
