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An efficient skewed line segmentation technique for cursive script OCR

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An efficient skewed line segmentation technique for cursive script OCR. / Malik, S.; Sajid, A.; Ahmad, A. et al.
In: Scientific Programming, Vol. 2020, 8866041, 04.12.2020.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Malik, S, Sajid, A, Ahmad, A, Almogren, A, Hayat, B, Awais, M & Kim, KH 2020, 'An efficient skewed line segmentation technique for cursive script OCR', Scientific Programming, vol. 2020, 8866041. https://doi.org/10.1155/2020/8866041

APA

Malik, S., Sajid, A., Ahmad, A., Almogren, A., Hayat, B., Awais, M., & Kim, K. H. (2020). An efficient skewed line segmentation technique for cursive script OCR. Scientific Programming, 2020, Article 8866041. https://doi.org/10.1155/2020/8866041

Vancouver

Malik S, Sajid A, Ahmad A, Almogren A, Hayat B, Awais M et al. An efficient skewed line segmentation technique for cursive script OCR. Scientific Programming. 2020 Dec 4;2020:8866041. doi: 10.1155/2020/8866041

Author

Malik, S. ; Sajid, A. ; Ahmad, A. et al. / An efficient skewed line segmentation technique for cursive script OCR. In: Scientific Programming. 2020 ; Vol. 2020.

Bibtex

@article{831a9ab5109f4b99aa6f0293f414a596,
title = "An efficient skewed line segmentation technique for cursive script OCR",
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. ",
keywords = "Optical character recognition, Arabic texts, Baseline detection, Context sensitivity, Cursive script, Handwritten dataset, Line segmentation, Printed texts, Recognition accuracy, Image segmentation",
author = "S. Malik and A. Sajid and A. Ahmad and A. Almogren and B. Hayat and M. Awais and K.H. Kim",
year = "2020",
month = dec,
day = "4",
doi = "10.1155/2020/8866041",
language = "English",
volume = "2020",
journal = "Scientific Programming",

}

RIS

TY - JOUR

T1 - An efficient skewed line segmentation technique for cursive script OCR

AU - Malik, S.

AU - Sajid, A.

AU - Ahmad, A.

AU - Almogren, A.

AU - Hayat, B.

AU - Awais, M.

AU - Kim, K.H.

PY - 2020/12/4

Y1 - 2020/12/4

N2 - 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.

AB - 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.

KW - Optical character recognition

KW - Arabic texts

KW - Baseline detection

KW - Context sensitivity

KW - Cursive script

KW - Handwritten dataset

KW - Line segmentation

KW - Printed texts

KW - Recognition accuracy

KW - Image segmentation

U2 - 10.1155/2020/8866041

DO - 10.1155/2020/8866041

M3 - Journal article

VL - 2020

JO - Scientific Programming

JF - Scientific Programming

M1 - 8866041

ER -