---
language:
  - my
  - pi
language_details: "my (Burmese), pi (Pali in Burmese script)"
license: cc-by-4.0
library_name: kraken
tags:
  - ocr
  - burmese
  - pali
  - text-recognition
  - kraken
  - myanmar
pretty_name: Burmese Kraken OCR
---

# Burmese Kraken OCR

A Kraken recognition model for **Burmese and Pali (in Burmese script)** text line
recognition. Trained from scratch on 897,883 rendered line images with augmentation
for real-world robustness.

## Model Description

- **Architecture**: CRNN (4 conv layers + 3× bidirectional LSTM(200) + linear output)
- **Parameters**: 4.0M
- **Alphabet**: 118 characters (Burmese consonants, vowels, tones, medials, digits,
  Pali combinations, punctuation)
- **Input**: grayscale/bi-level line image, height normalized to 120px
- **Output**: CTC-decoded Burmese/Pali text

## Performance

Evaluated on 10 held-out page images (6 modern Burmese, 4 Pali/nissaya):

| Category | CER |
|----------|-----|
| **Overall** | **0.77%** |
| Modern Burmese | 0.92% |
| Pali/nissaya | 0.60% |

## Training Details

- **Training data**: 718,515 lines (80% clean, 20% degraded with skew, speckle,
  blur, strokes, page-curve, perspective)
- **Dataset**: [`pndaza/burmese-kraken-1m`](https://huggingface.co/datasets/pndaza/burmese-kraken-1m)
- **Optimizer**: AdamW, learning rate 0.001 → 0.0001 (fine-tuning phase)
- **Precision**: 16-mixed (FP16)
- **Hardware**: NVIDIA RTX 5090 (vast.ai)
- **Val accuracy**: 0.9785

## Usage

### Command-line (kraken)

```bash
# Download the model
hf download pndaza/burmese-kraken-ocr burmese_kraken_ocr.safetensors

# Run OCR on a page image
kraken -i page.png output.txt binarize segment ocr -m burmese_kraken_ocr.safetensors

# For dense/degraded images, use the neural baseline segmenter
kraken -i page.png output.txt binarize segment -bl ocr -m burmese_kraken_ocr.safetensors
```

### Python (kraken API)

```python
from kraken import blla
from kraken.lib import vgsl
from kraken.lib.util import open_image

# Load model
model = vgsl.TorchVGSLModel.load_model("burmese_kraken_ocr.safetensors")

# Segment + recognize
im = open_image("page.png")
seg = blla.segment(im)
from kraken import rpred
recognizer = rpred.rpred(model, im, seg)
result = next(recognizer)
print(result)
```

## Notes

- This is a **recognition model** only. Segmentation uses Kraken's default
  `blla.mlmodel` baseline segmenter, which works well for most documents but
  may produce errors on dense Burmese text with deep descenders.
- Line images are rendered, not scanned. The training data includes degraded
  augmentations to bridge the domain gap, but performance on real scans may vary.
- For the **general-purpose image dataset** (not Kraken format), see
  [`pndaza/burmese-ocr-1m`](https://huggingface.co/datasets/pndaza/burmese-ocr-1m).