snd-handwritten-character-recognition-cnn

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.

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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)

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Performance

Test Accuracy

• Accuracy: 88.79%

Classification Report Summary

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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

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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

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

Applications

• Sindhi OCR systems
• Educational tools for Sindhi language learning
• Historical document digitization
• Research in handwritten character recognition

Citation

@misc{sindhi_cnn_2026,
  title        = {Sindhi Handwritten Character Classification using CNN},
  author       = {Danish Mahdi},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/DanishMahdi/snd-handwritten-character-recognition-cnn}}
}