DocumentCode :
1994131
Title :
Features for neural net based region identification of newspaper documents
Author :
Andersen, Tim ; Zhang, Wei
Author_Institution :
Comput. Sci. Dept., Boise State Univ., ID, USA
fYear :
2003
fDate :
3-6 Aug. 2003
Firstpage :
403
Abstract :
Several features for neural network based document region identification are tested. Specifically, this paper examines features for non-text region identification. The neural network based region identification algorithm is a key component of a document recognition system that segments a document into regions, classifies them into text, graphic, photo, and other region types, and then uses this classification to guide the processing and analysis of the image. The input data are unusually challenging: low quality images of newspaper documents obtained from microfilmed archives. The results compare favorably with other results reported in the literature.
Keywords :
document image processing; image recognition; image segmentation; information dissemination; neural nets; document classification; document recognition system; document segmentation; image analysis; image processing; image quality; microfilmed archives; neural net based region identification; neural network; newspaper documents; nontext region identification; region identification algorithm; Graphics; Image analysis; Image recognition; Image segmentation; Neural networks; Optical character recognition software; Pixel; Testing; Text analysis; Text recognition;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Document Analysis and Recognition, 2003. Proceedings. Seventh International Conference on
Print_ISBN :
0-7695-1960-1
Type :
conf
DOI :
10.1109/ICDAR.2003.1227698
Filename :
1227698
Link To Document :
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