Arabic handwriting recognition using baseline dependant features and hidden Markov modeling

Ramy El-Hajj, Laurence Likforman-Sulem, Chafic Mokbel

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this paper we describe a 1D HMM off-line 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.

Original languageEnglish
Title of host publicationProceedings of the Eighth International Conference on Document Analysis and Recognition
Pages893-897
Number of pages5
DOIs
Publication statusPublished - 1 Dec 2005
Event8th International Conference on Document Analysis and Recognition - Seoul, Korea, Republic of
Duration: 31 Aug 20051 Sept 2005

Publication series

NameProceedings of the International Conference on Document Analysis and Recognition, ICDAR
Volume2005
ISSN (Print)1520-5363

Conference

Conference8th International Conference on Document Analysis and Recognition
Country/TerritoryKorea, Republic of
CitySeoul
Period31/08/051/09/05

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