A Method of Recognition of Arabic Cursive Handwriting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image and Vision Computing
Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
Nearest neighbour searching in a picture archive system
International conference on Multimedia information systems '91
Extracting contours by perceptual grouping
Image and Vision Computing
Applying relevance feedback to a photo archival system
Journal of Information Science
Segmentation of planar curves into straight-line segments and elliptical arcs
Graphical Models and Image Processing
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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.