RECOGNITION OF OFF-LINE HANDWRITTEN ARABIC (INDIAN) NUMERALS USING MULTI-SCALE FEATURES AND SUPPORT VECTOR MACHINES VS. HIDDEN MARKOV MODELS

摘要

This paper describes a technique for automatic recognition of off-line handwritten Arabic (Indian) numerals using Support Vector Machines (SVM) and Hidden Markov Models (HMM). Local, intermediate, and large scale features are used. SVM parameters, producing the highest recognition rates, are experimentally found by using an exhaustive search algorithm. In addition, SVM classifier results are compared to those of the HMM classifier. The present research uses a database of 44 writers with 48 samples of each digit totaling 21120 samples. The SVM and HMM classifiers were trained with 75% of the data and tested with the remaining data. Other divisions of data for training and testing were performed and resulted in comparable performance. The achieved average recognition rates were 99.83% and 99.00% using, respectively, the SVM and HMM classifiers. SVM recognition rates proved to be better for all digits. Comparison at the writer's level (Writers 34 to 44) showed that SVM results outperformed HMM results for all tested writers. The classification errors of the SVM classifier were analyzed. The presented technique, using the powerful set of features and the SVM classifier, proved to be effective in the recognition of independent writer Arabic (Indian) numerals and was shown to be superior to the HMM classifier.