Snd Handwritten Character Recognition Cnn

--- language: - sd - en license: mit library_name: tensorflow pipeline_tag: image-classification tags: - cnn - tensorflow - keras - image-classification - handwritten-recognition - optical-character-recognition - ocr - sindhi - deep-learning model-index: - name: Sindhi Handwritten Character CNN results: - task: type: image-classification metrics: - type: accuracy value: 0.8879 name: Test Accuracy --- # Sindhi Handwritten Character Classification using CNN ## Model Overview This model is a Convolutional Neural Network (CNN) trained to classify **handwritten Sindhi characters**. It is designed for Optical Character Recognition (OCR) research and educational use. The model was trained on a custom dataset containing multiple handwritten samples of Sindhi alphabet characters. --- ## Model Details - **Framework:** TensorFlow / Keras - **Model Type:** Convolutional Neural Network (CNN) - **Task:** Multi-class image classification - **Input Type:** Grayscale handwritten character images - **Number of Classes:** 52 Sindhi characters - **Model Format:** `.h5` (Keras SavedModel) --- ## Performance ### Test Accuracy - **Accuracy:** 88.79% ### Classification Report Summary | Metric | Score | |--------|------:| | Precision (Macro Avg) | 0.897 | | Recall (Macro Avg) | 0.888 | | F1-Score (Macro Avg) | 0.887 | --- ## Training Summary - Initial learning rate: `0.001` - Reduced learning rates: `0.0005 → 0.00025` - Total epochs: 30 ### Training Behavior - Training accuracy increased steadily up to ~74% - Validation accuracy peaked around ~87–88% - Final model selected based on best validation performance --- ## Input Preprocessing The model expects images in the same format as training: - Convert to grayscale - Resize to training size (e.g., 64×64) - Normalize pixel values to `[0, 1]` - Add batch dimension ### Example ```python import cv2 import numpy as np from tensorflow.keras.models import load_model model = load_model("sindhi_cnn_best.h5") img = cv2.imread("test.png", cv2.IMREAD_GRAYSCALE) img = cv2.resize(img, (64, 64)) img = img.astype("float32") / 255.0 img = img.reshape(1, 64, 64, 1) pred = model.predict(img) class_id = np.argmax(pred) print(class_id) ``` ### Limitations - Works only for isolated handwritten characters - Not trained for full words or sentences - Performance depends on handwriting quality and dataset similarity ### Appl...

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