Discrete Cosine Transformation Based Approach for Offline Handwritten Character and Numeral Recognition

Abstract

Handwritten Character Recognition (HCR) of Indic scripts is one of the most challenging as compared other scripts. Recognition of handwritten character is more difficult one as compares with the printed one due to their similar structure and orientation. To have a proper recognition system for handwritten characters various researchers have adapted various methods and still yet to have more feasibility and high recognition rate to achieve. Here in this proposed work we have completely focused on handwritten characters and numerals of various scripts of different regions of India such as Bengali, Odia scripts. To have a proper solution for the ambiguities that arises in handwritten one and which has been resolved using the Discrete Cosine Transformation (DCT) as feature extraction part. This transformed based featured is used to calculated GLCM values of each images of handwritten numerals and characters. To maintain the novelty of the work in simulation part we have listed up variance nature of the individual’s images. Subsequently we have also harnessed the SVM, Neural Network, and Quadratic Classifier over dataset to report the recognition rate. After validating the proposed work, we can conclude that adopted model is quite helpful in building the automatic recognition system for both handwritten character and numerals to have solution for real time problems.