Optical Character Recognition of Balochi Script

Muhammad Mazhar; Qinbo; Dil Nawaz Hakro; Abdul Majid

doi:10.32628/CSEIT241046

Authors

Muhammad Mazhar Department of Computer Science and Technology, Faculty of Information Science and Technology, Ocean University of China Author
Qinbo Department of Computer Science and Technology, Faculty of Information Science and Technology, Ocean University of China Author
Dil Nawaz Hakro Faculty of Engineering and Technology (FET) University of Sindh, Jamshoro Author
Abdul Majid Department of Computer Science and Technology, Faculty of Information Science and Technology, Ocean University of China Author

DOI:

https://doi.org/10.32628/CSEIT241046

Keywords:

Optical Character Recognition, Balochi Script, Scale-Invariant Feature Transform, Long Shortterm Memory

Abstract

Optical Character Recognition is considered one of the fastest methods of data entry. OCR converts the text image representation of x and y coordinates representing pixel information to be converted into text data in a particular language. OCR as a field of pattern recognition and document image understanding, OCR requires a challenging job once a different language text is available on the image. Difference in language script will pose different challenges for OCR which requires entirely different approaches and algorithms. Latin scripts require a different approach whereas the Balochi adopted language scripts require a different approach. In this regard, various solutions have been proposed for different languages. Segmentation is considered one of the important tasks in the process of OCR. A good segmentation will definitely increase the accuracy of an OCR. Segmentation includes the segmentation of text lines from text images which are further divided into words. These segmented words are further divided into characters which are to be recognized. A single segmentation algorithm to segment various scripts of the languages is proposed in this study which checks the script and then segments the text image for the further processing in OCR. The proposed generalized algorithm will check the style, direction and other properties of the script and then adopts the segmentation process to segment text lines, words and characters of the language. The proposed algorithm segments more than ten languages of three scripts and segments for their OCRs. These images can be further processed for feature extraction and classification further. The process of OCR for selected languages will be made easier to recognize. Multiple scripts, languages and images were experimented, and the proposed algorithm successfully segmented 42,833 images of text line, words and character image. The algorithm provides 97% accuracy while segmenting these images and can be extended to further languages as well as scripts .

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