Automatic multi-lingual script recognition application

• Main Authors: Abdel Karim Abu-Ain, Waleed, Siti Norul Huda Sheikh Abdullah,, Khairuddin Omar,, Siti Zaharah Abd. Rahman,
• Format: Article
• Language: English
• Published: Penerbit Universiti Kebangsaan Malaysia 2018
• Online Access: http://journalarticle.ukm.my/17617/1/26556-83139-1-PB.pdf; http://journalarticle.ukm.my/17617/; https://ejournal.ukm.my/gema/issue/view/1098

Document Image Analysis and Recognition (DIAR) technique is used to recognize text component and translate it into editable format. Scripts are a set of graphical representations used to express a particular writing system as well as subsets belonging to a particular writing system. The writing styles of more than one script family may then be adopted by one language, such as in the cases where the old Malay language (Jawi) adopts the Arabic script while the modern one adopts the Roman script. The seven major scripts used in this research are in handwritten style including Arabic, Devanagari, Hebrew, Thai, Greek, Cyrillic and Korean. Automatic Multi-lingual Script Recognition (AMSR) is one of the main challenges in DIAR domain. Currently, only few attempts have been made for automated script identification of off-line handwritten documents images. Most available AMSR applications only deal with printed documents and script types, and they neglect handwritten and multi-lingual documents. The objective of this study is to propose a multi-lingual AMSR framework. The research methodology consists of a proposed multilingual AMSR framework. The multilingual AMSR framework is tested on Multilingual-HW datasets, which contains more than seven international unconstraint handwritten scripts, using Grey-Level Co-occurrence Matrix and Local Binary Pattern. The average accuracy of both methods is about 97.01% and 85.29% respectively. This proposed multilingual AMSR is hoped to be beneficial to a group of community which requires automatic sorting multi-lingual documents. This research can also be extended to document forensic area or international relations agency to identify unknown native document.