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Proceedings Paper

Examination paper text detection based on character discriminator
Author(s): Linteng Wang; Xiaoguang Li
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Paper Abstract

Examination paper text detection poses a challenging task of detecting text lines from examination paper with different sizes, low resolution, handwritten character and print character mixed, etc. In this paper, we propose a simple yet effective text detection method base on character discriminator (TDCD) to tackle the problem of examination paper text detection. We aim at detecting text lines of examination paper refer to high-quality character rectangles using the text line construction approach. To obtain the high-quality character rectangles, we firstly leverage canny filter and bounding rectangle tool for producing the bounding rectangles of examination paper connected regions. Then, the candidate rectangles generated by the merging of the bounding rectangles base on IOU. We finally propose a character discriminator (CD) to guide different candidate rectangles of a character to merge. Furthermore, we collect and annotate an examination paper image dataset EPDB-100. We conduct extensive experiments on EPDB-100. In particular, our TDCD achieves the F-measure score of 63.5%.

Paper Details

Date Published: 14 August 2019
PDF: 7 pages
Proc. SPIE 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019), 111791Z (14 August 2019); doi: 10.1117/12.2539618
Show Author Affiliations
Linteng Wang, Liaoning Univ. (China)
Xiaoguang Li, Liaoning Univ. (China)

Published in SPIE Proceedings Vol. 11179:
Eleventh International Conference on Digital Image Processing (ICDIP 2019)
Jenq-Neng Hwang; Xudong Jiang, Editor(s)

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