Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICDAR.2011.34
Title: A new gradient based character segmentation method for video text recognition
Authors: Shivakumara, P. 
Bhowmick, S.
Su, B.
Tan, C.L. 
Pal, U.
Keywords: Gradient features
Video character extraction
Video character recognition
Video document analysis
Word segmentation
Issue Date: 2011
Source: Shivakumara, P., Bhowmick, S., Su, B., Tan, C.L., Pal, U. (2011). A new gradient based character segmentation method for video text recognition. Proceedings of the International Conference on Document Analysis and Recognition, ICDAR : 126-130. ScholarBank@NUS Repository. https://doi.org/10.1109/ICDAR.2011.34
Abstract: The current OCR cannot segment words and characters from video images due to complex background as well as low resolution of video images. To have better accuracy, this paper presents a new gradient based method for words and character segmentation from text line of any orientation in video frames for recognition. We propose a Max-Min clustering concept to obtain text cluster from the normalized absolute gradient feature matrix of the video text line image. Union of the text cluster with the output of Canny operation of the input video text line is proposed to restore missing text candidates. Then a run length algorithm is applied on the text candidate image for identifying word gaps. We propose a new idea for segmenting characters from the restored word image based on the fact that the text height difference at the character boundary column is smaller than that of the other columns of the word image. We have conducted experiments on a large dataset at two levels (word and character level) in terms of recall, precision and f-measure. Our experimental setup involves 3527 characters of English and Chinese, and this dataset is selected from TRECVID database of 2005 and 2006. © 2011 IEEE.
Source Title: Proceedings of the International Conference on Document Analysis and Recognition, ICDAR
URI: http://scholarbank.nus.edu.sg/handle/10635/41838
ISBN: 9780769545202
ISSN: 15205363
DOI: 10.1109/ICDAR.2011.34
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