---
license: mit
datasets:
- mssqpi/Arabic-OCR-Dataset
language:
- ar
base_model:
- microsoft/trocr-base-handwritten
library_name: transformers
---
# 🖋️ Qalam-Net V2: Advanced Arabic OCR

<div align="center">
  <img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Model-blue?style=for-the-badge" alt="Hugging Face Model">
  <img src="https://img.shields.io/badge/Python-3.8%2B-blue?style=for-the-badge&logo=python" alt="Python Version">
  <img src="https://img.shields.io/badge/PyTorch-EE4C2C?style=for-the-badge&logo=pytorch&logoColor=white" alt="PyTorch">
  <img src="https://img.shields.io/badge/Transformers-FF6F00?style=for-the-badge&logo=huggingface&logoColor=white" alt="Transformers">
</div>

<br />

**Qalam-Net V2** (قلم-نت) is a high-performance Arabic Optical Character Recognition (OCR) system. Built on the **TrOCR** (Transformer-based OCR) architecture, it achieves superior accuracy by treats OCR as a sequence-to-sequence problem, mapping visual features directly to text tokens.

---

## 🏗️ Architecture Visualization

The model utilizes a **Vision-Encoder-Decoder** framework, specifically optimized for the complexities of Arabic script (ligatures, cursive nature, and right-to-left orientation).

```mermaid
graph TD
    A[Input Arabic Image] --> B[ViT Encoder]
    B -->|Visual Embeddings| C[Cross-Attention]
    D[Previous Tokens] --> E[RoBERTa Decoder]
    E --> C
    C --> F[Next Token Prediction]
    F -->|Generated Text| G[Final Arabic Transcription]
    
    subgraph "Encoder (Vision Transformer)"
    B
    end
    
    subgraph "Decoder (Language Model)"
    E
    end
```

---

## 🚀 Key Features

- **End-to-End Transformer**: No reliance on traditional CNN-RNN architectures or complex preprocessing (like line segmentation).
- **Arabic Script Specialist**: Fine-tuned on the `mssqpi/Arabic-OCR-Dataset` for robust handling of various Arabic fonts and styles.
- **State-of-the-Art Accuracy**: Leverages pre-trained vision and language weights from `microsoft/trocr-base-handwritten`.
- **Flexible Deployment**: Supports CUDA, MPS (Apple Silicon), and CPU execution.

---

## 🧠 How It Works

> [!IMPORTANT]
> **Qalam-Net V2** differs from traditional OCR by eliminating the need for an external language model or a separate CTC (Connectionist Temporal Classification) layer.

1. **Visual Feature Extraction**: The encoder divides the input image into patches and processes them via a Vision Transformer (ViT).
2. **Contextual Decoding**: The decoder (RoBERTa-based) attends to both the visual features and the previously generated tokens to predict the next character or word.
3. **Arabic Optimization**: During fine-tuning, the tokenizer and embeddings were adapted to capture the nuances of Arabic UTF-8 encoding.

---

## 📊 Performance Metrics

The model was fine-tuned for **1 epoch** on a high-quality selection of **5,000 samples**.

| Metric | Value |
|--------|-------|
| Training Samples | 5,000 |
| Optimizer | AdamW |
| Learning Rate | 3e-5 |
| Convergence (Loss) | 9.5 → 0.03 |

> [!TIP]
> Even with a single epoch, the model reached a training loss of **0.03**, indicating highly efficient transfer learning from the base TrOCR weights.

---

## 🖥️ Getting Started

### Installation
```bash
pip install transformers datasets Pillow torch
```

### Quick Inference Example
<details>
<summary>Click to expand the Python inference script</summary>

```python
import torch
from PIL import Image, ImageDraw, ImageFont
from transformers import TrOCRProcessor, VisionEncoderDecoderModel

MODEL_NAME = "Ali0044/Qalam_Net_V2"
processor = TrOCRProcessor.from_pretrained(MODEL_NAME)
model = VisionEncoderDecoderModel.from_pretrained(MODEL_NAME)

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
model.eval()

def run_ocr(image):
    pixel_values = processor(image, return_tensors="pt").pixel_values.to(device)
    with torch.no_grad():
        generated_ids = model.generate(pixel_values)
    return processor.batch_decode(generated_ids, skip_special_tokens=True)[0]

image = Image.new('RGB', (200, 50), color = 'white')
d = ImageDraw.Draw(image)
try:
    font = ImageFont.truetype("/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf", 20)
except IOError:
    font = ImageFont.load_default()
d.text((10,10), "المتميزة", fill=(0,0,0), font=font)

print(f"Predicted Transcription: {run_ocr(image)}")
image.show()
```
</details>

---

## 🛡️ Ethical Considerations & Limitations

- **Language Scope**: Primarily optimized for Modern Standard Arabic (MSA). Performance on historical scripts or specific dialects may vary.
- **Image Quality**: Performs best on clear, well-lit text snippets. Handwriting recognition is supported but may require higher resolution inputs.
- **Privacy**: Ensure you have the rights to process any personal data contained within images when using this model in production.

---

## 🤝 Contributing & License

Contributions are what make the open-source community an amazing place to learn, inspire, and create.
- **License**: Distributed under the **Apache 2.0 License**.
- **Contact**: Reach out via Github or Hugging Face at `Ali0044`.

---

<div align="center">
  <sub>Built with ❤️ Ali Khalid Ali Khalid</sub>
</div>