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title: Bec Dot.orc Api
emoji: 🚀
colorFrom: purple
colorTo: red
sdk: gradio
sdk_version: 6.5.1
app_file: app.py
pinned: false
license: apache-2.0
---
# Bec Dot.ocr API
OCR API powered by [rednote-hilab/dots.ocr](https://huggingface.co/rednote-hilab/dots.ocr) -- a multilingual document-parsing vision-language model. This Space provides both a browser UI and a programmatic API optimized for batch processing.
## Quick start
### 1. Install the client
```bash
pip install gradio_client
```
### 2. Process a single image
```python
from gradio_client import Client
client = Client("openpecha/bec-dot.orc-api")
result = client.predict(
"path/to/image.png", # local filepath or URL
"Extract the text content from this image.", # prompt
api_name="/predict",
)
print(result)
```
### 3. Batch-process many images
```python
import os
import json
from pathlib import Path
from gradio_client import Client, handle_file
client = Client("openpecha/bec-dot.orc-api")
image_dir = Path("images")
output_dir = Path("results")
output_dir.mkdir(exist_ok=True)
prompt = "Extract the text content from this image."
for img_path in sorted(image_dir.glob("*.png")):
print(f"Processing {img_path.name} ...")
result = client.predict(
handle_file(str(img_path)),
prompt,
api_name="/predict",
)
out_file = output_dir / f"{img_path.stem}.txt"
out_file.write_text(result, encoding="utf-8")
print(f" -> saved to {out_file}")
```
> **Tip:** The Space uses queuing (`max_size=20`), so requests are processed
> sequentially and will not time out even for large batches.
### 4. Use a custom prompt
The default prompt is `"Extract the text content from this image."` You can
override it for more specific tasks:
```python
# Layout-aware JSON extraction
result = client.predict(
handle_file("document.png"),
"""Please output the layout information from the PDF image, including each layout element's bbox, its category, and the corresponding text content within the bbox.
1. Bbox format: [x1, y1, x2, y2]
2. Layout Categories: ['Caption', 'Footnote', 'Formula', 'List-item', 'Page-footer', 'Page-header', 'Picture', 'Section-header', 'Table', 'Text', 'Title'].
3. Text Extraction & Formatting Rules:
- Picture: omit the text field.
- Formula: format as LaTeX.
- Table: format as HTML.
- All Others: format as Markdown.
4. Output the original text with no translation.
5. Sort all layout elements in human reading order.
6. Final Output: a single JSON object.""",
api_name="/predict",
)
```
## API reference
| Endpoint | Method | Parameters | Returns |
|---|---|---|---|
| `/predict` | POST | `image` (filepath/URL), `prompt` (string) | Raw text or JSON string |
## Model details
- **Model:** [rednote-hilab/dots.ocr](https://huggingface.co/rednote-hilab/dots.ocr) (1.7B LLM, ~3B total)
- **Precision:** bfloat16
- **Capabilities:** text extraction, layout detection, table recognition (HTML), formula parsing (LaTeX), multilingual support
|