Text Line Identification from a Multilingual Document

In India, a document may contain text lines in more than one language forms. For optical character recognition (OCR) of such a multilingual document, it is necessary to identify different language forms of the input document, before feeding the documents to the OCRs of individual language. In this paper, a simple but efficient technique of language identification for Kannada, Hindi and English text lines from a printed document is presented. The proposed system is based on the characteristic features of top-profile and bottom-profile of individual text lines of the input document image. The feature extraction is achieved by finding the behavior of the characteristics of the top and bottom profiles of individual text lines. The system is trained to learn the behavior of the top and bottom profiles with a training data set of 800 text lines. Range of feature values of top and bottom profiles for all the three languages are obtained and stored in knowledge base for later use during decision-making. For a new text line, necessary features are extracted from the top and bottom profiles and the feature values obtained are compared with the stored knowledge base. A new text line is classified to the type of the language that falls within that range. The proposed system is tested on 600 text lines and an overall classification accuracy of 96.6% is achieved.