Computer Science

Computer Science ›› 2020, Vol. 47 ›› Issue (11A): 409-415.doi: 10.11896/jsjkx.200100108

• Big Data & Data Science • Previous Articles     Next Articles

Duplicate Formula Detection Based on Deep Convolutional Neural Network

CHEN Ang1, TONG Wei1, ZHOU Yu-qiang2, YIN Yu2, LIU Qi2   

  1. 1 National Education Examinations Authority,Beijing 100084,China
    2 School of Computer Science and Technology,University of Science and Technology of China,Hefei 230026,China
  • Online:2020-11-15 Published:2020-11-17
  • About author:CHEN Ang,born in 1983,Ph.D,asso-ciate.His main research interests include data mining and educational big data.
    ZHOU Yu-qiang,born in 1998,postgraduate.His main research interests include machine learning and data mi-ning.
  • Supported by:
    This work was supported by the Foundation of National Education Examiantion Authorty for Scientific Research Plan of National Education Examination (GJK2017008) and National Natural Science Foundation of China (61672483).

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Abstract: In recent years,with the development of educational intelligence,the Internet education model has become an important carrier of education and teaching.Various online education systems provide learners with a convenient way to learn their vast amount of test resources.However,the accumulated exercise resources suffer from the high repetition rate and low quality due to various sources of test questions and inconsistent collection methods.Therefore,how to accurately and efficiently monitor test questions is an important way to refine network resources and improve the quality of network test questions.In this context,this paper focuses on the problem of repeated detection of picture formulas in science test resources.Through accurate formula recognition detection,it can eliminate the interference of test questions semantics,and then improve the test resource monitoring.In response to this problem,the traditional formula repeat detection method is often based on manually defined rules and difficult to apply to large-scale formula data detection because of cumbersome identification steps,low accuracy and low efficiency.Based on this,this paper proposes a formula repeated detection method based on deep convolutional neural network.Firstly,a multi-channel convolution mechanism is used to automate the extraction and processing of formula picture features,making it suitable for large-scale formula data detection.Then,using the end-to-end output mode,the accumulation of errors that may be caused by too many intermediate steps in the traditional method is avoided.Finally,in order to verify the accuracy and practicability of the model,this paper has carried out sufficient experiments on the standard test data set and the data set of the simulated scan noise.The experimental results show that this method can effectively process the formula pictures of different quality.Good results in both accuracy and efficiency.

Key words: Convolutional neural network, Duplicate formula detection, Exercise quality, Image recognition

CLC Number: 

  • TP301
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