2011 International Conference on Communications, Computing and Control Applications (CCCA)Comment: 5 page

The choice of relevant features is very decisive in handwriting recognition rate. Our aim is to present some useful structural and statistical features and see their degree of variability. In this paper, we start with a description of the variability of the Arabic handwriting and the way how to reduce it. Four kinds of feature sets used by our handwriting systems are then presented evaluated and discussed. The comparison is carried on a database of images from IFN/ENIT databases. The Neural Network Multilayer perceptrons is our method of classification. A contrastive study of these primitives is done according to recognition their time and memory consuming and their variability degree.

Recognition of Arabic Character field has been gaining more interest for many years, and a large number of research papers and reports have already been published in this area. There are several major issues with Arabic character recognition: Arabic characters are spelled differently (depending on whether they are isolated, at the beginning, in the middle or at the end of the word), multiple characters can have the same body but a number and/or position of various diacritics. The size of the Arabic characters may vary from one writer to another and even within the writing of a single writer, etc. This paper presents a new approach for feature extraction step of online handwritten Arabic character using global and local features. The system was tested with 2000 Characters written by different writers and the best rate of recognition obtained was 92.43%.

No abstract

Handwriting recognition is a challenge that interests many researchers around the world. As an exception, handwritten Arabic script has many objectives that remain to be overcome, given its complex form, their number of forms which exceeds 100 and its cursive nature. Over the past few years, good results have been obtained, but with a high cost of memory and execution time. In this paper we propose to improve the capacity of bidirectional gated recurrent unit (BGRU) to recognize Arabic text. The advantages of using BGRUs is the execution time compared to other methods that can have a high success rate but expensive in terms of time and memory. To test the recognition capacity of BGRU, the proposed architecture is composed by 6 convolutional neural network (CNN) blocks for feature extraction and 1 BGRU + 2 dense layers for learning and test. The experiment is carried out on the entire database of institut für nachrichtentechnik/ecole nationale d'ingénieurs de Tunis (IFN/ENIT) without any preprocessing or data selection. The obtained results show the ability of BGRUs to recognize handwritten Arabic script.