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AuthorHassen H.
AuthorAl-Maadeed, Somaya
Available date2022-05-19T10:23:12Z
Publication Date2017
Publication Name1st IEEE International Workshop on Arabic Script Analysis and Recognition, ASAR 2017
ResourceScopus
Identifierhttp://dx.doi.org/10.1109/ASAR.2017.8067764
URIhttp://hdl.handle.net/10576/31136
AbstractDue to the variability of writing styles and to other problems related to the nature of Arabic scripts, the recognition of Arabic handwriting is still awaiting accurate results. Segmentation of Arabic handwritten words into graphemes poses a major challenge in Arabic handwriting recognition and is highly error prone. In this paper, we adopt the holistic approach which handles the whole word image without any segmentation step. A set of different statistical features were investigated in this paper, namely, the Invariant Moments (IV), Histogram of Oriented Gradients (HOG) and the Gabor features. The classifier used is the Sequential Minimal Optimization (SMO) algorithm which is an improvement of the Support Vector Machines (SVM). The dataset used is AHDB which consists of 3045 images containing the most commonly used Arabic words written by one hundred different writers. The application of the features used with the SMO algorithm resulted in 91.5928 % correct classification.
SponsorThis publication was made possible by NPRP grant # NPRP NPRP7-442-1-082 from Qatar National Research fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectImage segmentation
Optimization
Support vector machines
Arabic handwriting
Arabic handwriting recognition
Handwritten words
Histogram of oriented gradients (HOG)
Holistic approach
Sequential minimal optimization
Sequential minimal optimization algorithms
Statistical features
Character recognition
TitleArabic handwriting recognition using sequential minimal optimization
TypeConference Paper
Pagination79-84
dc.accessType Abstract Only


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