Hog Features Based Handwritten Bengali Numerals Recognition Using SVM Classifier: A Comparison with Hopfield Implementation
Parul Gahelot, Pradeepta Kumar Sarangi, Merry Saxena, Jayant Jha, Amit Vajpayee, Ashok Kumar Sahoo
Abstract
Handwritten digit recognition (reading by computer) is a process that gives the technological capabilities to a system to recognize handwritten numerals using Machine Learning. It is very tough to recognize handwritten characters because of the non-similarity in characters by different people. Also, there exists no standard technique available in the industry which can be applicable to provide innovative solutions to all kinds of handwritten characters. Hence, there is a requirement to understand the nature of the script. In order to complete the task, there is a need to use unique extraction methods and a classifier. This work implements a Histogram of Oriented Gradient (HOG) features with Support Vector Machine (SVM) classifier and a Hopfield model to recognize handwritten Bengali numerals into the class of ten segments. SVM works in the way of mapping the input elements to a set of the high-dimensional feature vector. This works both in the case of linearly and nonlinearly and separable data. The experiments have been done using 1200 handwritten digits for 10 different classes (0 to 9). The overall accuracy (productivity) obtained from the implementation is 89.5%. The experimental outcomes from the Hopfield model give an accuracy of 95.5%. Also, it is found the Hopfield model is more capable of handling noisy inputs.