Title: Neural-Based Segmentation Technique for Arabic Handwriting Scripts
Authors: Al Hamad, Husam A.
Citation: WSCG 2013: Communication Papers Proceedings: 21st International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision in co-operation with EUROGRAPHICS Association, p. 9-14.
Issue Date: 2013
Publisher: Václav Skala - UNION Agency
Document type: konferenční příspěvek
URI: http://wscg.zcu.cz/WSCG2013/!_2013-WSCG-Communications-proceedings.pdf
ISBN: 978-80-86943-75-6
Keywords: arabské ruční písmo;rozpoznávání obrazu;neuronové sítě;heuristický segmentér
Keywords in different language: arabic handwriting;image recognition;neural networks;heuristic segmenter
Abstract: In some algorithms, segmentation of the word image considers the first step of the recognition processes; the main aim of this paper is proposed new fusion equations for improving the segmentation of word image. The technique that has used is divided into two phases; at the beginning, applying the Arabic Heuristic Segmenter (AHS), AHS uses the shape features of the word image, it employs three features, remove the punctuation marks (dots), ligature detection, and finally average character width, the goal of this technique is placed the Prospective Segmentation Points (PSP) in the whole parts of the word image. As a result, the second phase apply the neuralbased segmentation technique, the goal of neural technique is check and examine all PSPs in the word image in order to report which one is valid or invalid, this will increase the accuracy of the segmentation; to do that, the network obtains a fused value from three neural confidences values: 1) Segmentation Point Validation (SPV), 2) Right Character Validation (RCV), and 3) Central Character Validation (CCV) which will assess each PSP separately. The input vectors of the neural network are calculated based on Direction Feature (DF), DF considers much more suitable for Arabic Scripts. AHS and neural-based segmentation techniques have been implemented and tested by local benchmark database.
Rights: © Václav Skala - UNION Agency
Appears in Collections:WSCG 2013: Communication Papers Proceedings

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