Analysis of the reforming languages by image-based variations of LBP and NBP operators

This paper proposes an extension of the local binary pattern and neighbor binary pattern as a basis for extracting features needed for recognizing an image which represents a text in specific languages. At the first, the unicode text is, according to its energy status in the text-line area, converted into a gray level image. Then, the extension of the local binary pattern and neighbor binary pattern is proposed. These features are extracted in order to differentiate image-based representations of a text in a given language. At the end, the extracted features are classified by Support Vector Machine and Naive Bayes to establish a difference that can identify different languages. The obtained results prove the accuracy and efficiency of the proposed method when compared with other state-of-the-art methods.