Khmer Handwritten Digits Recognition using Convolution Neural Networks

This study focuses on developing a Convolutional Neural Network (CNN) model to recognize and classify handwritten Khmer numbers from a dataset of 19,530 images. The research addresses the challenge of duplicated number recognition by leveraging CNNs, which are highly effective for image recognition tasks in computer vision. The dataset is preprocessed, cleaned, scaled, and split into training, validation, and testing sets. Using libraries such as NumPy, Pandas, TensorFlow, Keras, and Scikit-learn, a CNN model is constructed, trained, and evaluated, achieving a 95% accuracy in predicting handwritten Khmer numbers from 0 to 9. The work highlights the efficiency and robustness of CNNs compared to other networks for this task, contributing to improved handwritten number recognition.