Content Based Indexing and Retrieval in a Digital Library of Arabic Scripts and Calligraphy

Abstract

Due the cursive nature of the Arabic scripts automatic recognition of keywords using computers is very difficult. Content based indexing using textual, graphical and visual information combined provides a more realistic and practical approach to the problem of indexing large collection of calligraphic material. Starting with low level patter recognition and feature extraction techniques, graphical representations of the calligraphic material can be captured to form the low level indexing parameters. These parameters are then enhanced using textual and visual information provided by the users. Through visual feedback and visual interaction, recognized textual information can be used to enhance the indexing parameter and in return improve the retrieval of the calligraphic material. In this paper, we report an implementation of the system and show how visual feedback and visual interaction helps to improve the indexing parameters created using the low-level image feature extraction technologies.KeywordsVisual FeedbackEdge PointOptical Character RecognitionCurve SegmentHough TransformThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.