By: Prof. Archana Vyas, Vedanshi Pandya, and Yashvi Desai
1.Prof. Archana Vyas , Assistant Professor, Department of Information Technology, Dharmsinh Desai University, Gujarat, India
2.Vedanshi Pandya,Student, Department of Information Technology, Dharmsinh Desai University, Gujarat, India
3. Yashvi Desai, Student, Department of Information Technology, Dharmsinh Desai University, Gujarat, India
Handwritten digit recognition is a fundamental task in the field of pattern recognition and computer vision with numerous applications. The development of deep learning algorithms has greatly increased the efficiency and accuracy of recognition of handwritten digits systems. However, the choice of optimizer plays a crucial role in determining the convergence rate and final performance of deep learning models. In this study, we provide a thorough analysis of many deep learning optimizers for the task of handwriting digit recognition in Gujarati. The study focuses on optimizing the recognition accuracy and convergence speed of convolutional neural network (CNN) models trained on a dataset comprising handwritten digits in the Gujarati script.
KEYWORDS: Deep Learning, Gujarati Numerals, Handwritten script recognition, CNN, Gradient descent, RMSprop, Adam, Adagrad, Adadelta.
Citation:
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