README.md

---
language: ar
license: apache-2.0
tags:
- ocr
- arabic
- keras
- jax
- tensorflow
- pytorch
datasets:
- mssqpi/Arabic-OCR-Dataset
---

<div align="center">
  <img src="https://huggingface.co/Ali0044/Qalam-Net/resolve/main/banner.png" width="100%" alt="Qalam-Net Banner">
  
  # 🖋️ Qalam-Net (قلم-نت)
  ### *High-Performance, Cross-Backend Arabic OCR*
  
  [![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
  [![Framework](https://img.shields.io/badge/Framework-Keras%203-F14B5C.svg)](https://keras.io/)
  [![Backend](https://img.shields.io/badge/Backend-JAX%20|%20TF%20|%20Torch-blueviolet.svg)](https://keras.io/keras_3/)
</div>

---

## 🌟 Highlights
- **🚀 Ultra-Fast Inference**: Native JAX/XLA support for accelerated processing.
- **🧩 Portable Architecture**: Patched (v2) to resolve serialization issues across Keras versions.
- **🎯 Precision Driven**: CNN + BiLSTM + Self-Attention pipeline optimized for Arabic script.
- **🔓 Unified Loading**: No custom layers or complex setup required for inference.

---

## 📖 How it Works
The model processes Arabic text images through a sophisticated multi-stage pipeline:

```mermaid
graph LR
    A[Input Image 128x32] --> B[CNN Backbone]
    B --> C[Spatial Features]
    C --> D[Dual BiLSTM]
    D --> E[Self-Attention]
    E --> F[Softmax Output]
    F --> G[NumPy CTC Decoder]
    G --> H[Arabic Text]
```

---

## 🚀 Quick Start (Robust Usage)

Use the following implementation to run inference on any platform. This uses a custom **NumPy-based decoder** for 100% cross-backend compatibility.

<details>
<summary><b>View Python Implementation</b></summary>

```python
import os
os.environ["KERAS_BACKEND"] = "jax" # Options: "jax", "tensorflow", "torch"

import keras
import numpy as np
import cv2
from huggingface_hub import hf_hub_download

class QalamNet:
    def __init__(self, repo_id="Ali0044/Qalam-Net"):
        # 1. Download and Load Model
        print(f"Loading Qalam-Net from {repo_id}...")
        model_path = hf_hub_download(repo_id=repo_id, filename="model.keras")
        self.model = keras.saving.load_model(model_path)
        
        # 2. Define the exact 38-character Arabic Vocabulary
        # [ALIF, BA, TA, THA, JEEM, HAA, KHAA, DAL, THAL, RA, ZAY, SEEN, SHEEN, SAD, DAD, TAA, ZAA, AIN, GHAIN, FA, QAF, KAF, LAM, MEEM, NOON, HA, WAW, YA, TEH_MARBUTA, ALEF_MAKSURA, ALEF_HAMZA_ABOVE, ALEF_HAMZA_BELOW, ALEF_MADDA, WAW_HAMZA, YEH_HAMZA, HAMZA, SPACE, TATWEEL]
        self.vocab = ['ا', 'ب', 'ت', 'ث', 'ج', 'ح', 'خ', 'د', 'ذ', 'ر', 'ز', 'س', 'ش', 'ص', 'ض', 'ط', 'ظ', 'ع', 'غ', 'ف', 'ق', 'ك', 'ل', 'م', 'ن', 'ه', 'و', 'ي', 'ة', 'ى', 'أ', 'إ', 'آ', 'ؤ', 'ئ', 'ء', ' ', 'ـ']
        
    def preprocess(self, image_path):
        img = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)
        img = cv2.resize(img, (128, 32)) / 255.0
        img = img.T # Transpose for CRNN architecture
        img = np.expand_dims(img, axis=(-1, 0))
        return img.astype(np.float32)

    def predict(self, image_path):
        batch_img = self.preprocess(image_path)
        preds = self.model.predict(batch_img) # Output shape: (1, 32, 39)
        
        # 3. NumPy-based CTC Greedy Decoding (Cross-Backend)
        argmax_preds = np.argmax(preds, axis=-1)[0]
        
        # Remove consecutive duplicates
        unique_indices = [argmax_preds[i] for i in range(len(argmax_preds)) 
                          if i == 0 or argmax_preds[i] != argmax_preds[i-1]]
        
        # Remove blank index (index 38)
        blank_index = preds.shape[-1] - 1
        final_indices = [idx for idx in unique_indices if idx != blank_index]
        
        # Map to vocabulary
        return "".join([self.vocab[idx] for idx in final_indices if idx < len(self.vocab)])

# Usage
ocr = QalamNet()
print(f"Predicted Arabic Text: {ocr.predict('/content/images.png')}")
```
</details>

---

## 📊 Performance & Metrics
Training was conducted on the **mssqpi/Arabic-OCR-Dataset** over 50 epochs.

| Metric | Value |
| :--- | :--- |
| **Input Shape** | 128 x 32 x 1 (Grayscale) |
| **Output Classes** | 39 (38 Chars + 1 Blank) |
| **Final Loss** | ~13.13 |
| **Val Loss** | ~89.79 |
| **Framework** | Keras 3.x (Native) |

## 📁 Dataset
This model was trained on the **[Arabic-OCR-Dataset](https://huggingface.co/datasets/mssqpi/Arabic-OCR-Dataset)** provided by **Muhammad AL-Qurishi (mssqpi)**.
- **Total Samples**: ~2.16 Million images.
- **Content**: A massive collection of Arabic text lines in various fonts and styles.
- **Usage**: Used for training the CRNN architecture to recognize sequential Arabic script.

---

## 🤝 Acknowledgments
Developed and maintained by **[Ali Khalid](https://github.com/Ali0044)**. This model is part of a comparative research study on Arabic OCR architectures.

---

> [!TIP]
> **Pro Tip**: Use the **JAX** backend for the fastest inference times on modern CPUs and GPUs!