Hybrid Neural Network and Genetic Algorithm for off-Lexicon Online Arabic Handwriting Recognition

被引：5

作者：

Hamdi, Yahia ^{[1
]}

Chaabouni, Aymen ^{[1
]}

Boubaker, Houcine ^{[1
]}

Alimi, Adel M. ^{[1
]}

机构：

[1] Univ Sfax, Natl Sch Engineers, REGIM Lab, REs Grp Intelligent Machines, BP 1173, Sfax 3038, Tunisia

来源：

PROCEEDINGS OF THE 16TH INTERNATIONAL CONFERENCE ON HYBRID INTELLIGENT SYSTEMS (HIS 2016) | 2017年 / 552卷

关键词：

Hybrid recognition system; Off-lexicon recognition; Online Arabic handwriting; Neural networks; Genetic algorithm; GRAPHEMES SEGMENTATION;

D O I：

10.1007/978-3-319-52941-7_43

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper we propose the hybridization of neural networks and genetic algorithm for online Arabic handwriting recognition. The used method consists in decomposing the input signal into continuous parts called graphemes based on Beta-Elliptical model and baseline detection. The segmented graphemes are then described according to their position in the pseudo-word by a combination of geometric features modeling their trajectory shape and provided in the input of the neural networks used for graphemes class recognition. Finally, a genetic algorithm is used to generate the characters code corresponding to the obtained chain of recognized graphemes code by applying the genetic search process: selection, crossover and mutation. The developed system is evaluated using an Arabic words dataset extracted from the ADAB Database.

引用

页码：431 / 441

页数：11

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