Ensembling: Model of histogram of oriented gradient based handwritten devanagari character recognition system

摘要

It is very easy for the humans to recognize a character after gaining some basic knowledge about the language, but when it comes to the computer, the computer cannot recognize the character until and unless it is trained properly. We need to transfer the knowledge to the computer to so that the computer can automatically recognize the character based on the knowledge provided. The Devanagari script is used as a base language for many different languages. This paper presents the system which recognize the handwritten devanagari isolated characters using ensembling of classifiers. In this paper HWCR system to recognize the Devanagari character using ensembling of classifiers is proposed. Recognition of Devanagari character is done in three main steps. The first step is pre-processing of the character image in which binarization and complementary of an image are performed. The second important step is feature extraction for which it uses histogram of oriented gradient as a feature. Third step is classification in which three different classifiers are used and their performance are analysed. The three classifiers are SVM, K-NN and N N. Results of these classifiers are combined together and given to the ensembler who classifies the class label based on maximum voting method. The average recognition rate is achieved by the proposed HWCR system is 88.13% using ensembling.