Visualizing and Understanding Customized Convolutional Neural Network for Recognition of Handwritten Marathi Numerals

Abstract

Numeral recognition is one of the most indispensable applications in pattern recognition. Recognizing numerals, written in Indian languages is a demanding problem. Devanagari Marathi is one such popular Indian language script, and perceiving Marathi numerals written in different patterns, is a challenging task. Depending on the type of feature extraction, varied approaches dealing with numeral recognition, have been suggested and practiced on smaller data-sets. However, no standard large data-set is available for handwritten Marathi numerals. Therefore, a data-set with 80000 samples has been prepared for proposed work. This paper proposes a Customized Convolutional Neural Network (CCNN) that has the ability to learn the features automatically and predict the class of numerals from a wide ranged data-set. Additionally, visualization of the intermediate CCNN layers is presented that explains the dynamics of the presented network. Out of 80000 numerals, written in Marathi, 70000 samples are used for training and 10000, for testing. The CCNN's performance when verified using K- fold cross validation has achieved average 94.93% accuracy for testing data-sets.