Bangla language modeling algorithm for automatic recognition of hand-sign-spelled Bangla sign language

作     者：Muhammad Aminur RAHAMAN Mahmood JASIM Md.Haider ALI Md.HASANUZZAMAN Muhammad Aminur RAHAMAN;Mahmood JASIM;Md.Haider ALI;Md.HASANUZZAMAN

作者机构：Department of Computer Science and EngineeringUniversity of DhakaDhaka-1000Bangladesh 

出 版 物：《Frontiers of Computer Science》 (中国计算机科学前沿（英文版）)

年 卷 期：2020年第14卷第3期

页      面：45-64页

核心收录：

学科分类：081203[工学-计算机应用技术] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported and funded by the Information and Communication Technology(ICT)Division Ministry of Posts Telecommunications and IT Government of the People’s Republic of Bangladesh 

主　　题：Bangla sign language(BdSL) hand-sign classification Bangla language modeling rules(BLMR) Bangla language modeling algorithm(BLMA) 

摘      要：Because of using traditional hand-sign segmentation and classification algorithm,many diversities of Bangla language including joint-letters,dependent vowels *** representing 51 Bangla written characters by using only 36 hand-signs,continuous hand-sign-spelled Bangla sign language(BdSL)recognition is *** paper presents a Bangla language modeling algorithm for automatic recognition of hand-sign-spelled Bangla sign language which consists of two *** phase is designed for hand-sign classification and the second phase is designed for Bangla language modeling algorithm(BLMA)for automatic recognition of hand-sign-spelled Bangla sign *** first phase,we have proposed two step classifiers for hand-sign classification using normalized outer boundary vector(NOBV)and window-grid vector(WGV)by calculating maximum inter correlation coefficient(ICC)between test feature vector and pre-trained feature *** first,the system classifies hand-signs using *** classification score does not satisfy specific threshold then another classifier based on WGV is *** system is trained using 5,200 images and tested using another(5,200×6)images of 52 hand-signs from 10 signers in 6 different challenging environments achieving mean accuracy of 95.83%for classification with the computational cost of 39.972 milliseconds per *** the Second Phase,we have proposed Bangla language modeling algorithm(BLMA)which discovers allhidden charactersbased onrecognized charactersfrom 52 hand-signs of BdSL to make any Bangla words,composite numerals and sentences in BdSL with no training,only based on the result of first *** the best of our knowledge,the proposed system is the first system in BdSL designed on automatic recognition of hand-sign-spelled BdSL for large *** system is tested for BLMA using hand-sign-spelled 500 words,100 composite numerals and 80 sentences in BdSL achieving mean accuracy of 93.50%,95.50%and 90.50%respectively.