---
license: mit
tags:
  - scene-text-recognition
  - scene-text-detection
  - ocr
  - indic
  - paddleocr
library_name: paddleocr
---

# IndicSTSpotter — model weights

Detector + recognizer for **IndicSTSpotter**, a unified multilingual scene-text spotter
covering **11 Indic scripts + Latin** with a single detector, a single recognizer, and one
**frozen 1,638-character dictionary** — no script identification, no per-language weights.

> Private while the paper is under review.

## Contents

| Path | Model | Params | Notes |
|---|---|---|---|
| `rec_stage2/` | recognizer (SVTR + CTC head, `PP-OCRv6_medium_rec`) | 15.8M | synthetic pretrain → real fine-tune; the paper's headline recognizer |
| `det_mist/` | detector (DB, `PP-OCRv6_medium_det`) | 15.5M | trained on real + MIST; used for dense-scene detection & end-to-end |
| `unified_dict.txt` | — | — | the frozen 1,638-char dictionary shared by all recognizers |

## Usage

```python
from huggingface_hub import snapshot_download
d = snapshot_download("MahimaSinghal/IndicSTSpotter", repo_type="model")

from paddleocr import TextRecognition, TextDetection
rec = TextRecognition(model_name="PP-OCRv6_medium_rec", model_dir=f"{d}/rec_stage2")
det = TextDetection(model_name="PP-OCRv6_medium_det", model_dir=f"{d}/det_mist")
```

For dense scenes, run the detector at a higher inference resolution (long side 1920).
See the code repository's `REPRODUCE.md` for the exact per-table evaluation commands.

## License

MIT (code). Model weights released for research use.