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README.md

@@ -12,11 +12,11 @@ tags:
 
 # Qalam-Net (قلم-نت): Advanced Arabic OCR
 
-Qalam-Net is a high-performance, cross-backend Optical Character Recognition (OCR) model for Arabic. Built on **Keras 3**, it supports **JAX**, **PyTorch**, and **TensorFlow** backends for seamless deployment across any infrastructure.
+Qalam-Net is a high-performance, cross-backend Optical Character Recognition (OCR) model for Arabic. Built on **Keras 3**, it supports **JAX**, **PyTorch**, and **TensorFlow** backends.
 
 ## 🚀 Quick Start (Advanced Usage)
 
-The following example demonstrates how to use **JAX** for ultra-fast XLA-accelerated inference.
+To use Qalam-Net, you must define the `CTCLayer` (used during training) and pass it as a custom object.
 
 ### 1. Installation
 ```bash
@@ -29,16 +29,32 @@ import os
 os.environ["KERAS_BACKEND"] = "jax" # Switch to "tensorflow" or "torch" if preferred
 
 import keras
+from keras import layers
 import numpy as np
 import cv2
 from huggingface_hub import hf_hub_download
 
+# [MANDATORY] Define the CTCLayer for deserialization
+@keras.saving.register_keras_serializable()
+class CTCLayer(layers.Layer):
+    def __init__(self, name=None, **kwargs):
+        super().__init__(name=name, **kwargs)
+        self.loss_fn = keras.backend.ctc_batch_cost
+
+    def call(self, y_true, y_pred):
+        return y_pred
+
 class QalamNet:
     def __init__(self, repo_id="Ali0044/Qalam-Net"):
-        # Download and load the latest model from Hugging Face
-        print(f"Fetching model from {repo_id}...")
+        # Download the model
         model_path = hf_hub_download(repo_id=repo_id, filename="model.keras")
-        self.model = keras.saving.load_model(model_path)
+        
+        # Load with custom_objects
+        self.model = keras.saving.load_model(
+            model_path, 
+            custom_objects={"CTCLayer": CTCLayer},
+            compile=False
+        )
         
         # Standard Arabic Vocabulary
         self.vocab = [' ', '!', '"', '#', '(', ')', '*', '+', ',', '-', '.', '/', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', ':', ';', '=', '?', '[', ']', 'ء', 'آ', 'أ', 'ؤ', 'إ', 'ئ', 'ا', 'ب', 'ة', 'ت', 'ث', 'ج', 'ح', 'خ', 'د', 'ذ', 'ر', 'ز', 'س', 'ش', 'ص', 'ض', 'ط', 'ظ', 'ع', 'غ', 'ـ', 'ف', 'ق', 'ك', 'ل', 'م', 'ن', 'ه', 'و', 'ى', 'ي', 'ً', 'ٌ', 'ٍ', 'َ', 'ُ', 'ِ', 'ّ', 'ْ', '٠', '١', '٢', '٣', '٤', '٥', '٦', '٧', '٨', '٩']
@@ -53,19 +69,19 @@ class QalamNet:
 
     def predict(self, image_path):
         batch_img = self.preprocess(image_path)
-        predictions = self.model.predict(batch_img)
+        # The model has 2 inputs [image, label] but we only need image for prediction
+        # We can pass dummy labels or use the internal prediction layers
+        preds = self.model.predict([batch_img, np.zeros((1, 1))])
         
-        # CTC Decode (Greedy)
-        input_len = np.ones(predictions.shape[0]) * predictions.shape[1]
-        results = keras.backend.ctc_decode(predictions, input_length=input_len, greedy=True)[0][0]
+        # CTC Decode
+        input_len = np.ones(preds.shape[0]) * preds.shape[1]
+        results = keras.backend.ctc_decode(preds, input_length=input_len, greedy=True)[0][0]
         
-        # Map indices to characters
-        text = "".join([self.vocab[int(res)] for res in results[0] if res != -1])
-        return text
+        return "".join([self.vocab[int(res)] for res in results[0] if res != -1])
 
 # Usage
 # ocr = QalamNet()
-# print(f"Predicted: {ocr.predict('image.png')}")
+# print(ocr.predict("text.png"))
 ```
 
 ## 🧠 Model Architecture
@@ -73,7 +89,6 @@ Qalam-Net employs a specialized **CNN-BiLSTM-Attention** pipeline:
 - **Spatial Features**: 3-block CNN for robust feature extraction.
 - **Sequence Context**: Dual Bidirectional LSTMs to capture Arabic script flow.
 - **Focus Mechanism**: Self-attention layer for character-level precision.
-- **Decoding**: Connectionist Temporal Classification (CTC).
 
 ---
 **Developed by [Ali Khalid](https://github.com/Ali0044)**