Abstract
Convolutional neural network (CNN) based methods have succeeded for handwritten numeral recognition (HNR) applications. However, CNN seems to misclassify similarly shaped numerals (i.e., the silhouette of the numerals that look the same). This paper presents an enhanced HNR system to improve the classification accuracy of the similarly shaped handwritten numerals incorporating the terminals points with CNN’s recognition, which can be utilized in various emerging applications related to language translation. In handwritten numerals, the terminal points (i.e., the start and end positions) are considered additional properties to discriminate between similarly shaped numerals. Start–End Writing Measure (SEWM) and its integration with CNN is the main contribution of this research. Traditionally, the classification outcome of a CNN-based system is considered according to the highest probability exposed for a particular numeral category. In the proposed system, along with such classification, its probability value (i.e., CNN’s confidence level) is also used as a regulating element. Parallel to CNN’s classification operation, SEWM measures the start-end points of the numeral image, suggesting the numeral category for which measured start-end points are found close to reference start-end points of the numeral class. Finally, the output label or system’s classification of the given numeral image is provided by comparing the confidence level with a predefined threshold value. SEWM-CNN is a suitable HNR method for Bengali and Devanagari numerals compared with other existing methods.
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering
Reference48 articles.
1. Online and off-line handwriting recognition: A comprehensive survey;Plamondon;IEEE Trans. Pattern Anal. Mach. Intell.,2000
2. Handwritten Bangla numeral recognition system and its application to postal automation;Wen;Pattern Recognit.,2007
3. A genetic algorithm based region sampling for selection of local features in handwritten digit recognition application;Das;Appl. Soft Comput.,2012
4. Hand Written Bangla Numerals Recognition for Automated Postal System;Nasir;IOSR J. Comput. Eng.,2013
5. Hassan, T., and Khan, H.A. (2015, January 21–23). Handwritten Bangla numeral recognition using Local Binary Pattern. Proceedings of the 2015 International Conference on Electrical Engineering and Information Communication Technology (ICEEICT), Dhaka, Bangladesh.
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