---
license: apache-2.0
language:
- fa
library_name: paddleocr
pipeline_tag: image-to-text
base_model: PaddlePaddle/PP-OCRv6_medium_rec
datasets:
- Reza2kn/persian-handwriting-pages-3.69m
- Reza2kn/persian-printed-ocr-3.5m
tags:
- ocr
- persian
- handwriting
- printed-text
- paddlepaddle
- pp-ocrv6
---

# Bina 0.2 - RizehPizeh

Bina 0.2 - RizehPizeh is a compact Persian line-recognition model fine-tuned
from PP-OCRv6 medium recognition. It recognizes both handwritten and printed
Persian text using a 161-symbol character dictionary and a CTC decoder.

This repository preserves the earlier experimental checkpoints under
`checkpoints/`. The verified release is at the repository root.

## Verified release

| Held-out split | Rows | Exact lines | Exact-line accuracy | Mean normalized edit similarity |
| --- | ---: | ---: | ---: | ---: |
| Combined | 4,096 | 3,474 | **84.81%** | **98.81%** |
| Handwriting | 2,048 | 1,758 | **85.84%** | **98.83%** |
| Printed | 2,048 | 1,716 | **83.79%** | **98.79%** |

Exact-line accuracy is strict: one incorrect character, digit, space, or
punctuation mark makes the entire line incorrect. The independent evaluator
accepted all 4,096 transforms without replacement.

## Package contents

- `inference/`: exported Paddle inference graph and parameters.
- `bina_text_recognition.py`: logical-order Persian inference wrapper.
- `Bina-0.2-RizehPizeh.pdparams`: best training checkpoint.
- `config.yml`: portable architecture and preprocessing configuration.
- `persian_arabic_bina02_dict.txt`: 161-symbol character dictionary.
- `MODEL_PROVENANCE.json`: revisions, counts, metrics, and SHA-256 hashes.
- `eval-aligned-row-analysis-v2.json`: independent row-level evaluation.

## Inference

Install the appropriate PaddlePaddle build for your hardware, then:

```bash
pip install "paddleocr>=3.3.0,<4"
git clone https://huggingface.co/Reza2kn/Bina-0.2-RizehPizeh
cd Bina-0.2-RizehPizeh
python bina_text_recognition.py path/to/line.jpg --device cpu
```

Or use it from Python:

```python
from bina_text_recognition import BinaTextRecognition

model = BinaTextRecognition("inference", device="cpu")
for result in model.predict("line.jpg"):
    print(result["text"], result["score"])
```

The exported CTC model emits Persian in visual order. Use the included wrapper
to recover logical reading order while preserving Latin and numeric runs.

## Input contract

The recognizer expects a horizontal, content-tight line crop:

- Rotate tall detector crops 90 degrees clockwise.
- Remove excessive printed-image background while retaining a small margin.
- Resize to `3 x 48 x 768` with `padding: false`.
- Preserve ZWNJ and Persian digits.

For full-page OCR, pair this recognizer with
[`PaddlePaddle/PP-OCRv6_medium_det_safetensors`](https://huggingface.co/PaddlePaddle/PP-OCRv6_medium_det_safetensors),
rectify and normalize each detected crop, recognize each line, then reconstruct
the page in reading order. This repository contains the recognizer, not the
detector.

## Training

The verified release used 169,000 balanced training lines:

- 84,500 teacher-aligned handwriting crops.
- 84,500 printed line images.
- 4,096 page-isolated held-out evaluation lines.

Training stopped automatically after validation plateaued. The best checkpoint
was selected around step 47,520. Dataset revisions and preprocessing details
are pinned in `MODEL_PROVENANCE.json`.

## Limitations

- Persian-focused; other-language performance was not preserved or evaluated.
- Full-page use requires a separate detector and reading-order reconstruction.
- The handwriting split is teacher-aligned and may contain residual label noise.
- Very small isolated symbols and unusually degraded crops remain difficult.

## License

Apache License 2.0. See `LICENSE`.