Efficient segmentation of sub-words within handwritten arabic words
Author | Khan F. |
Author | Bouridane A. |
Author | Khelifi F. |
Author | Almotaeryi R. |
Author | Al-Maadeed, Somaya |
Available date | 2022-05-19T10:23:13Z |
Publication Date | 2014 |
Publication Name | Proceedings - 2014 International Conference on Control, Decision and Information Technologies, CoDIT 2014 |
Resource | Scopus |
Identifier | http://dx.doi.org/10.1109/CoDIT.2014.6996979 |
Abstract | Segmentation is considered as a core step for any recognition or classification method and for the text within any document to be effectively recognized it must be segmented accurately. In this paper a text and writer independent algorithm for the segmentation of sub-words in Arabic words has been presented. The concept is based around the global binarization of an image at various thresholding levels. When each sub-word or Part of Arabic Word (PAW) within the image being investigated is processed at multiple threshold levels a cluster graph is obtained where each cluster represents the individual sub-words of that word. Once the clusters are obtained the task of segmentation is managed by simply selecting the respective cluster automatically which is achieved using the 95% confidence interval on the processed data generated by the accumulated graph. The presented algorithm was tested on 537 randomly selected words from the AHTID/MW database and the results showed that 95.3% of the sub-words or PAW were correctly segmented and extracted. The proposed method has shown considerable improvement over the projection profile method which is commonly used to segment sub-words or PAW. |
Language | en |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Subject | Information retrieval systems Text processing Classification methods Confidence interval Global binarization Multiple threshold Projection profile Sub words Thresholding Character recognition |
Type | Conference Paper |
Pagination | 684-689 |
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Computer Science & Engineering [2284 items ]