Bangla Handwritten Character Recognition Using Local Binary Pattern and Its Variants

Optical Character Recognition (OCR) especially for handwritten characters is an important task for its numerous applications in daily life including data digitizing, robotics vision, helping visually disabled people and many more. However, Bangla Handwritten Character Recognition (HCR) is rarely explored despite Bangla being one of the mostly spoken languages over the world. For classifying Bangla basic characters, compound characters and digits various feature descriptors and classification algorithms can be used. This paper provides a comparative study of different Local Binary Pattern (LBP) based feature descriptors on Bangla basic characters, compound characters and digits. For classification, Support Vector Machine (SVM) with linear kernel is used. The rigorous experiments on CMATERdb 3.1.2, CMATERdb 3.1.3.1 and CMATERdb 3.1.1 datasets for Bangla basic characters, Bangla compound characters and Bangla digits respectively have showed reasonable accuracies of different LBP based feature descriptors.