DASNUS-Kurdish-ParagraphHTR/README.md

---
language:
  - ckb
license: cc-by-nc-4.0
tags:
  - handwritten-text-recognition
  - paragraph-recognition
  - ckb
  - densenet
  - transformer
  - pytorch
  - safetensors
datasets:
  - DASNUS
metrics:
  - cer
  - wer
pipeline_tag: image-to-text
---

# DASNUS-Kurdish: DenseNet121-Transformer Paragraph HTR

## Model Description
Kurdish handwritten paragraph recognition model fine-tuned on the external DASNUS dataset accessed through: https://data.mendeley.com/datasets/xdj9f55rkm/1. Pre-trained on 12,000 synthetic Kurdish paragraphs from DASTNUS, then fine-tuned on 1,843 reconstructed DASNUS paragraphs. Demonstrates cross-dataset transfer capability.

## Architecture
- **CNN Backbone:** DenseNet-121 (pretrained on ImageNet)
- **Horizontal Upsample:** Yes
- **Encoder:** 3 Transformer encoder layers
- **Decoder:** 6 Transformer decoder layers
- **Attention Heads:** 8
- **Hidden Size:** 256
- **Feed-Forward Dim:** 2048
- **Vocabulary Size:** 116
- **Parameters:** 22,746,927

## Performance on DASNUS
| Metric | Value |
|--------|-------|
| CER (greedy) | 0.0856 |
| WER (greedy) | 0.3148 |

## Input Format
- **Image size:** 600 x 1235 pixels
- **Preprocessing:** Aspect-ratio-preserving resize, right-aligned on white canvas (RTL)
- **Normalization:** ImageNet mean/std

## Training
- **Pre-training:** 12,000 synthetic paragraph images with curriculum learning
- **Fine-tuning:** Real handwritten paragraphs from DASNUS
- **Two-stage strategy:** Encoder frozen for first 10 epochs during fine-tuning

## Usage
```python
from safetensors.torch import load_file
import json

# Load model weights
state_dict = load_file("model.safetensors")

# Load config
with open("config.json", "r") as f:
    config = json.load(f)

# Load vocabulary
with open("vocab.json", "r") as f:
    vocab = json.load(f)

# Load reverse mapping
with open("idx_to_char.json", "r") as f:
    idx_to_char = json.load(f)
```

## Citation
```
[Citation to be added upon publication]
```

## License
This model is released under CC-BY-NC-4.0 for non-commercial research purposes only.