NDLI: Labelling logical structures of document images using a dynamic perceptive neural network

Content Provider	Springer Nature Link
Author	Rangoni, Yves Belaïd, Abdel Vajda, Szilárd
Copyright Year	2011
Abstract	This paper proposes a new method for labelling the logical structures of document images. The system starts with digitised images of paper documents, performs a physical layout analysis, runs an OCR and finally exploits the OCR’s outputs to find the meaning of each block of text (i.e. assigns labels like “Title”, “Author”, etc.). The method is an extension of our previous work where a classifier, the perceptive neural network, has been developed to be an analogy of the human perception. We introduce in this connectionist model a temporal dimension by the use of a time-delay neural network with local representation. During the recognition stage, the system performs several recognition cycles and corrections, while keeping track and reusing the previous outputs. This dynamic classifier allows then a better handling of noise and segmentation errors. The experiments have been carried out on two datasets: the public MARG containing more than 1,500 front pages of scientific papers with four zones of interest and another one composed of documents from the Siggraph 2003 conference, where 21 logical structures have been identified. The error rate on MARG is less than 2.5% and 7.3% on the Siggraph dataset.
Starting Page	45
Ending Page	55
Page Count	11
File Format	PDF
ISSN	14332833
Journal	International Journal of Document Analysis and Recognition (IJDAR)
Volume Number	15
Issue Number	1
e-ISSN	14332825
Language	English
Publisher	Springer-Verlag
Publisher Date	2011-03-03
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Document image analysis and recognition Layout analysis Logical labelling Perceptive neural network Time-delay neural network Image Processing and Computer Vision Pattern Recognition
Content Type	Text
Resource Type	Article
Subject	Computer Science Applications Computer Vision and Pattern Recognition Software

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