Machine Learning Based Handwritten Character Recognition

Abstract

  Handwritten character recognition (HCR) is a vital field in pattern recognition and machine learning, with wide applications ranging from postal services to automated form processing. The comparative study of various methods used for HCR, highlighting both traditional and deep learning approaches is presented in this paper. Conventional techniques, such as k-Nearest Neighbors (k-NN), Support Vector Machines (SVM), and Artificial Neural Networks (ANN), are compared with modern deep learning architectures like Convolutional Neural Networks (CNNs). The study examines the efficiency, accuracy, and complexity of these methods, focusing on their performance in recognizing handwritten characters in diverse datasets. Key challenges in HCR, such as variations in handwriting styles, noise, and distortions in the images, are discussed. Additionally, the importance of pre-processing techniques, such as normalization, binarization, and feature extraction, is emphasized for improving recognition rates. The results of the study show that while traditional methods are effective for smaller datasets with minimal variations, deep learning models, particularly CNNs, outperform in terms of accuracy and generalization on large, complex datasets. The paper concludes by discussing the future potential of combining multiple models and using hybrid techniques for further improvement in HCR systems.