An efficient skewed line segmentation technique for cursive script OCR

Malik, S. and Sajid, A. and Ahmad, A. and Almogren, A. and Hayat, B. and Awais, M. and Kim, K.H. (2020) An efficient skewed line segmentation technique for cursive script OCR. Scientific Programming, 2020.

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Abstract

Segmentation of cursive text remains the challenging phase in the recognition of text. In OCR systems, the recognition accuracy of text is directly dependent on the quality of segmentation. In cursive text OCR systems, the segmentation of handwritten Urdu language text is a complex task because of the context sensitivity and diagonality of the text. This paper presents a line segmentation algorithm for Urdu handwritten and printed text and subsequently to ligatures. In the proposed technique, the counting pixel approach is employed for modified header and baseline detection, in which the system first removes the skewness of the text page, and then the page is converted into lines and ligatures. The algorithm is evaluated on manually generated Urdu printed and handwritten dataset. The proposed algorithm is tested separately on handwritten and printed text, showing 96.7% and 98.3% line accuracy, respectively. Furthermore, the proposed line segmentation algorithm correctly extracts the lines when tested on Arabic text.

Item Type:
Journal Article
Journal or Publication Title:
Scientific Programming
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1700/1706
Subjects:
ID Code:
150454
Deposited By:
Deposited On:
12 Jan 2021 16:35
Refereed?:
Yes
Published?:
Published
Last Modified:
16 Jun 2021 08:51