Implementation of an Optical Character Reader (OCR) for Bengali language
Date
2016-03Publisher
© 2016 Institute of Electrical and Electronics Engineers Inc.Metadata
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Chowdhury, M. T., Islam, M. S., Bipul, B. H., & Rhaman, M. K. (2015). Implementation of an optical character reader (OCR) for bengali language. Paper presented at the Proceedings of 2015 International Conference on Data and Software Engineering, ICODSE 2015, 126-131. doi:10.1109/ICODSE.2015.7436984Abstract
Optical Character Recognition (OCR) is the process of extracting text from an image. The main purpose of an OCR is to make editable documents from existing paper documents or image files. Significant number of algorithms is required to develop an OCR and basically it works in two phases such as character and word detection. In case of a more sophisticated approach, an OCR also works on sentence detection to preserve a document's structure. It has been found that researchers put lots of efforts for developing a Bengali OCR but none of them is completely error free. To take this issue in consideration, the latest 3.03 version of Tesseract OCR engine for Windows operating system is used to develop an OCR for Bengali language. Moreover, 18110 characters and 2617 words are used to make the OCR's library. In this research, 'Solaimanlipi' font and 200 input files are used to test the accuracy of OCR. It is found that for clean image files, the accuracy of the software is as high as 97.56%. It is to be noted that accuracy is measured as the percentage of correct characters and words.
Description
This conference paper was presented in the International Conference on Data and Software Engineering, ICODSE 2015; Universitas Gadjah MadaYogyakarta; Indonesia; 25 November 2015 through 26 November 2015 [© 2015 IEEE] The conference paper's definite version is available at: http://dx.doi.org/10.1109/ICODSE.2015.7436984Publisher Link
http://ieeexplore.ieee.org/document/7436984/Department
Department of Computer Science and Engineering, BRAC UniversityType
Conference paperCollections
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