Arabic and Latin Script Identification in Printed and Handwritten Types Based on Steerable Pyramid Features

Arabic and Latin script identification in printed and handwritten nature present several difficulties because the Arabic (printed or handwritten) and the handwritten Latin scripts are cursive scripts of nature. To avoid all possible confusions which can be generated, we propose in this paper an accurate and suitable designed system for script identification at word level which is based on steerable pyramid transform. The features extracted from pyramid sub bands serve to classify the scripts on only one script among the scripts to identify. The encouraging and promising results obtained are presented in this research paper.

[1]  Edward H. Adelson,et al.  The Design and Use of Steerable Filters , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Sridha Sridharan,et al.  Texture for script identification , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Yue Lu,et al.  Bangla/English Script Identification Based on Analysis of Connected Component Profiles , 2006, Document Analysis Systems.

[4]  Patrick Kelly,et al.  Automatic Script Identification From Document Images Using Cluster-Based Templates , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  David S. Doermann,et al.  Identifying script on word-level with informational confidence , 2005, Eighth International Conference on Document Analysis and Recognition (ICDAR'05).

[6]  Henry S. Baird,et al.  Language identification in Complex, Unoriented, and Degraded Document Images , 1996, DAS.

[7]  Adel M. Alimi,et al.  Three decision levels strategy for Arabic and Latin texts differentiation in printed and handwritten natures , 2007 .

[8]  A. Alimi,et al.  Script Identification for Arabic and Latin, Printed and Handwritten Documents , 2005 .

[9]  Adel M. Alimi,et al.  Une approche de discrimination arabe / latin, imprimé / manuscrit , 2000 .

[10]  Patrick Kelly,et al.  Script and language identification for handwritten document images , 1999, International Journal on Document Analysis and Recognition.

[11]  Sally L. Wood,et al.  Language identification for printed text independent of segmentation , 1995, Proceedings., International Conference on Image Processing.

[12]  Jie Ding,et al.  Classification of oriental and European scripts by using characteristic features , 1997, Proceedings of the Fourth International Conference on Document Analysis and Recognition.

[13]  Ching Y. Suen,et al.  Script identification using steerable Gabor filters , 2005, Eighth International Conference on Document Analysis and Recognition (ICDAR'05).

[14]  U. Pal,et al.  Multi-script line identification from Indian documents , 2003, Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings..

[15]  Tieniu Tan,et al.  Rotation Invariant Texture Features and Their Use in Automatic Script Identification , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  Fu Chang,et al.  Language identification of character images using machine learning techniques , 2005, Eighth International Conference on Document Analysis and Recognition (ICDAR'05).

[17]  Kuo-Chin Fan,et al.  Classification Of Machine-Printed And Handwritten Texts Using Character Block Layout Variance , 1998, Pattern Recognit..

[18]  Adel M. Alimi,et al.  Script and nature differentiation for Arabic and Latin text images , 2002, Proceedings Eighth International Workshop on Frontiers in Handwriting Recognition.

[19]  A. Lawrence Spitz,et al.  Determination of the Script and Language Content of Document Images , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[20]  William T. Freeman,et al.  Presented at: 2nd Annual IEEE International Conference on Image , 1995 .

[21]  N. V. Subbareddy,et al.  Neural network based system for script identification in Indian documents , 2002 .