An efficient CNN model with squirrel optimizer for handwritten digit recognition

Abstract

The modern way of handwritten digit recognition has various potential real-time applications like management of students' score sheets, reading the tax form, postal code reading, and cheque processing [1]. Generally, diverse numeral strings are made of overlapping, segregated, connected, or wrecked digits. Segmentation, feature selection, and feature extraction of digit handwritten images with diverse numeral string lengths are analyzed by various researchers [2]. *Author for correspondence Those existing approaches are designed to predict the unconstrained handwritten digit which includes all the four pre-processing stages before classification.The most challenging tasks with this prediction model are segmentation and feature extraction as these steps pretend to give better classification accuracy of isolated digits [3].Generally, the numbers of digits over, the strings are unknown and therefore the optimal boundary between them is extremely complex to identify. Some existing Re search Article