Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/41523
Title: Script and language identification in degraded and distorted document images
Authors: Lu, S. 
Tan, C.L. 
Issue Date: 2006
Source: Lu, S.,Tan, C.L. (2006). Script and language identification in degraded and distorted document images. Proceedings of the National Conference on Artificial Intelligence 1 : 769-774. ScholarBank@NUS Repository.
Abstract: This paper reports a statistical identification technique that differentiates scripts and languages in degraded and distorted document images. We identify scripts and languages through document vectorization, which transforms each document image into an electronic document vector that characterizes the shape and frequency of the contained character and word images. We first identify scripts based on the density and distribution of vertical runs between character strokes and a vertical scan line. Latin-based languages are then differentiated using a set of word shape codes constructed using horizontal word runs and character extremum points. Experimental results show that our method is tolerant to noise, document degradation, and slight document skew and attains an average identification rate over 95%. Copyright © 2006, American Association for Artificial Intelligence (www.aaai.org). All rights reserved.
Source Title: Proceedings of the National Conference on Artificial Intelligence
URI: http://scholarbank.nus.edu.sg/handle/10635/41523
ISBN: 1577352815
Appears in Collections:Staff Publications

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