Innovative CNN Strategies for Superior Handwritten Digit Recognition - Shreyash Tarade; Sneha Tanpure; Sanket Pawar; Shreeharsh Puntambekar (International Journal of VLSI Circuit Design & Technology, 2024)
Handwritten digit recognition is a fundamental problem in the field of computer vision and machine learning with numerous applications, such as postal code recognition, bank check processing, and digitizing historical documents. Convolutional Neural Networks have demonstrated remarkable success in various image recognition tasks, making them a popular choice for digit recognition. In this study, we present an enhanced approach to handwritten digit recognition using CNNs. Handwritten digit recognition plays a pivotal role in various applications, including optical character recognition, digit based and digitization of historical documents. Our method incorporates several novel techniques and optimizations to achieve superior accuracy and efficiency in digit recognition tasks. We begin by preprocessing the input images to enhance their quality and reduce noise. Subsequently, we employ a deep CNN architecture with multiple convolutional and pooling layers to extract hierarchical features from the digit images. Batch normalization and dropout layers are strategically applied to improve convergence and reduce overfitting. Additionally, we introduce data augmentation methods, such as rotation, scaling, and translation, to improve the model’s generalization capabilities and reduce overfitting.
Keywords: Convolutional neural network; Enhanced digit recognition; Epochs; Handwritten digit recognition; image recognition