Devanagari Handwritten Character Recognition using Convolutional Neural Networks

Abstract

Devanagari is an Indic script and forms a basis for over 100 languages spoken in India and Nepal including Hindi, Marathi, Sanskrit, and Maithili. It consists of 47 primary alphabets, 14 vowels, 33 consonants, and 10 digits. In addition, the letters of the alphabet are modified when a vowel is added to a consonant. There is no capitalization of letters, like Latin languages. The devanagari script consists of consonants and modifiers. This paper presents a system that works on a set of 29 consonants and one modifier. It uses a self-made Devanagari script dataset which comprises of 29 consonants with no header line (Shirorekha) over them. The dataset has 34604 handwritten images. Deep learning techniques are applied to extract features and recognize the characters in an image. Deep Convolutional Neural Network (DCNN) have been incorporated to extract features and classify the input images. Consecutive convolutional layers are used in this process which brings added advantage in the process of extracting higher-level features. The trained model demonstrated an accuracy of 99.65%.