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|Title:||Arabic handwriting recognition using baseline dependant features and hidden Markov modeling||Authors:||Hajj Mohamad, Ramy Al
|Affiliations:||Department of Electrical Engineering||Keywords:||Handwriting recognition
Hidden Markov models
|Subjects:||Image segmentation||Issue Date:||2005||Part of:||Eighth International Conference on Document Analysis and Recognition (ICDAR'05)||Start page:||1||End page:||5||Conference:||International Conference on Document Analysis and Recognition (ICDAR) (8th : 31 Aug- 1 Sep 2005 : Seoul, South Korea)||Abstract:||
In this paper, we describe a 1D HMM offline handwriting recognition system employing an analytical approach. The system is supported by a set of robust language independent features extracted on binary images. Parameters such as lower and upper baselines are used to derive a subset of baseline dependent features. Thus, word variability due to lower and upper parts of words is better taken into account. In addition, the proposed system learns character models without character pre-segmentation. Experiments that have been conducted on the benchmark IFN/ENIT database of Tunisian handwritten country/village names, show the advantage of the proposed approach and of the baseline-dependant features.
|URI:||https://scholarhub.balamand.edu.lb/handle/uob/392||Ezproxy URL:||Link to full text||Type:||Conference Paper|
|Appears in Collections:||Department of Electrical Engineering|
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