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Savitri Jadhav, Vandana Inamdar. Convolutional Neural Network and Histogram of Oriented Gradient Based Invariant Handwritten MODI Character Recognition. PATTERN RECOGNITION AND IMAGE ANALYSIS 2022. [DOI: 10.1134/s1054661822020109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Liang QK, Peng JZ, Li ZW, Xie DQ, Sun W, Wang YN, Zhang D. Robust table recognition for printed document images. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2020; 17:3203-3223. [PMID: 32987525 DOI: 10.3934/mbe.2020182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
The recognition and analysis of tables on printed document images is a popular research field of the pattern recognition and image processing. Existing table recognition methods usually require high degree of regularity, and the robustness still needs significant improvement. This paper focuses on a robust table recognition system that mainly consists of three parts: Image preprocessing, cell location based on contour mutual exclusion, and recognition of printed Chinese characters based on deep learning network. A table recognition app has been developed based on these proposed algorithms, which can transform the captured images to editable text in real time. The effectiveness of the table recognition app has been verified by testing a dataset of 105 images. The corresponding test results show that it could well identify high-quality tables, and the recognition rate of low-quality tables with distortion and blur reaches 81%, which is considerably higher than those of the existing methods. The work in this paper could give insights into the application of the table recognition and analysis algorithms.
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Affiliation(s)
- Qiao Kang Liang
- College of Electrical and Information Engineering, Hunan University, Changsha 410082, China
- National Engineering Laboratory for Robot Vision Perception and Control, Hunan University, Changsha 410082, China
| | - Jian Zhong Peng
- College of Electrical and Information Engineering, Hunan University, Changsha 410082, China
- National Engineering Laboratory for Robot Vision Perception and Control, Hunan University, Changsha 410082, China
| | - Zheng Wei Li
- Department of Mechanical Engineering, University of Alberta, Edmonton, AB T6G 2R3, Canada
| | - Da Qi Xie
- College of Electrical and Information Engineering, Hunan University, Changsha 410082, China
- National Engineering Laboratory for Robot Vision Perception and Control, Hunan University, Changsha 410082, China
| | - Wei Sun
- College of Electrical and Information Engineering, Hunan University, Changsha 410082, China
- National Engineering Laboratory for Robot Vision Perception and Control, Hunan University, Changsha 410082, China
| | - Yao Nan Wang
- College of Electrical and Information Engineering, Hunan University, Changsha 410082, China
- National Engineering Laboratory for Robot Vision Perception and Control, Hunan University, Changsha 410082, China
| | - Dan Zhang
- Department of Mechanical Engineering, York University, Toronto, ON M3J 1P3, Canada
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