Handwritten Mixed Numerals Classification System

Optical Character Recognition is a growing field in pattern recognition, where we can find lots of work done on different Indic languages such as Tamil, Telugu, Gujarati, Bangla, Kannada, etc. Previous work for OCR based on Indic languages show the need for compound OCR system for Indic languages, which can able to deal with documents having details in multiple languages. The proposed system for the classification of handwritten mixed numerals can overcome the limitations of the existing monolingual system based on OCR. We trained a model to identify numerals of Hindi and Gujarati languages. Here we used the projection profile technique as feature extraction over 22,400 images of numerals to generate a feature set. We tried different classifiers such as Naïve Bayes classifier for multi-class classification, Support Vector Machine, K-Nearest Neighbors, and Multi-Layer Perceptron network over the train and test image datasets having a ratio of 70:30 to evaluate and select the best model with the highest performance. We have successfully achieved an accuracy of 66.94%, 90.78%, 84.61%, and 88.21% using mentioned classifiers for Gujarati-Hindi mixed handwritten numerals.