Adaptive Residual Attention Network for Handwritten Character and Digits Recognition with Improved Energy Valley Optimizer Algorithm
Srinivasa Rao N, C. Nelson Kennedy Babu · 2024
One of the very significant issues in pattern recognition applications is handwritten character recognition. Digit recognition is used in a variety of applications, such as form data entry, postal mail sorting and bank check processing. The capacity to create an effective algorithm that can identify handwritten numbers that are submitted by users via digital devices, tablets, and scanners. Handwritten digit recognition in computer vision systems is a challenging task and it is essential for many emerging applications. Therefore, a handwritten character and digits recognition technique is developed. The images related to handwritten characters are collected through the internet. Then, the images are given to the recognition phase. The handwritten character and digit recognition are performed using the Adaptive Residual Attention Network (ARAN) model. Here, the parameter tuning is performed using the Improved Energy Valley Optimization Algorithm (IEVOA) for enhancing the recognition performance. Hence, the suggested handwritten character and digits recognition model effectively recognizes the handwritten digits and characters than the traditional systems. The experimental outcomes are compared with other models.