A deep learning based system for mathematical expression detection and recognition in document images

被引:0
作者
Bui Hai Phong [1 ,3 ]
Loung Tan Da [1 ]
Nguyen Thi Yen [2 ]
Thang Math Hoang [2 ]
Thi-Lan Le [1 ,2 ]
机构
[1] Hanoi Univ Sci & Technol, MICA Int Res Inst, Hanoi, Vietnam
[2] Hanoi Univ Sci & Technol, Sch Elect & Commun, Hanoi, Vietnam
[3] Hanoi Architectural Univ, Fac Informat Technol, Hanoi, Vietnam
来源
2020 12TH INTERNATIONAL CONFERENCE ON KNOWLEDGE AND SYSTEMS ENGINEERING (IEEE KSE 2020) | 2020年
关键词
Mathematical expression detection; Mathematical expression recognition; Deep learning; YOLO; Neural Network;
D O I
10.1109/kse50997.2020.9287693
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Detection and recognition of mathematical expressions in document images are two key steps for the development of a mathematical expression retrieval system. So far, many researches have proposed for the recognition of expressions. However, few systems have integrated the detection and recognition of expressions in document images. This paper presents a deep learning based system for mathematical expression detection and recognition. Firstly, mathematical expressions have been detected on document images using the You Only Look Once (YOLO) v3 network. Then, detected expressions have been recognized in an end-to-end way using the advanced neural network that is the Watch, Attend and Parse (WAP). The proposed system has been tested on the Marmot public dataset. The obtained accuracies of the detection of isolated and inline expressions are 93% and 73%, respectively. Meanwhile, accuracies of the recognition for isolated and detected expressions are 51.77% and 45.50%, respectively. The results have shown the promising application of our preliminary research.
引用
收藏
页码:85 / 90
页数:6
相关论文
共 22 条
  • [1] A Low Complexity Sign Detection and Text Localization Method for Mobile Applications
    Bouman, Katherine L.
    Abdollahian, Golnaz
    Boutin, Mireille
    Delp, Edward J.
    [J]. IEEE TRANSACTIONS ON MULTIMEDIA, 2011, 13 (05) : 922 - 934
  • [2] The OCRopus open source OCR system
    Breuel, Thomas M.
    [J]. DOCUMENT RECOGNITION AND RETRIEVAL XV, 2008, 6815
  • [3] A Hybrid Method for Mathematical Expression Detection in Scientific Document Images
    Bui Hai Phong
    Thang Manh Hoang
    Thi-Lan Le
    [J]. IEEE ACCESS, 2020, 8 : 83663 - 83684
  • [4] Mathematical Variable Detection based on Convolutional Neural Network and Support Vector Machine
    Bui Hai Phong
    Thang Manh Hoang
    Thi-Lan Le
    [J]. 2019 INTERNATIONAL CONFERENCE ON MULTIMEDIA ANALYSIS AND PATTERN RECOGNITION (MAPR), 2019,
  • [5] Mathematical Formula Detection in Heterogeneous Document Images
    Chu, Wei-Ta
    Liu, Fan
    [J]. 2013 CONFERENCE ON TECHNOLOGIES AND APPLICATIONS OF ARTIFICIAL INTELLIGENCE (TAAI), 2013, : 140 - 145
  • [6] The Pascal Visual Object Classes (VOC) Challenge
    Everingham, Mark
    Van Gool, Luc
    Williams, Christopher K. I.
    Winn, John
    Zisserman, Andrew
    [J]. INTERNATIONAL JOURNAL OF COMPUTER VISION, 2010, 88 (02) : 303 - 338
  • [7] Garain Utpal, 2009, 2009 10th International Conference on Document Analysis and Recognition (ICDAR), P1340, DOI 10.1109/ICDAR.2009.203
  • [8] Deep Residual Learning for Image Recognition
    He, Kaiming
    Zhang, Xiangyu
    Ren, Shaoqing
    Sun, Jian
    [J]. 2016 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2016, : 770 - 778
  • [9] He WH, 2016, INT C PATT RECOG, P3246, DOI 10.1109/ICPR.2016.7900135
  • [10] Mathematical expression recognition: A survey
    Chan K.-F.
    Yeung D.-Y.
    [J]. International Journal on Document Analysis and Recognition, 2000, 3 (1) : 3 - 15