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All Journal Perfecting a Video Game with Game Metrics Bulletin of Electrical Engineering and Informatics
Tajbia Karim
Bangladesh University of Professionals
Computer Science & IT Electrical & Electronics Engineering

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Automated Bangla sign language translation system for alphabets by means of MobileNet Tazkia Mim Angona; A. S. M. Siamuzzaman Shaon; Kazi Tahmid Rashad Niloy; Tajbia Karim; Zarin Tasnim; S. M. Salim Reza; Tasmima Noushiba Mahbub
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 3: June 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i3.15311

Abstract

Individuals with hearing and speaking impairment communicate using sign language. The movement of hand, body and expressions of face are the means by which the people, who are unable to hear and speak, can communicate. Bangla sign alphabets are formed with one or two hand movements. There are some features which differentiates the signs. To detect and recognize the signs, analyzing its shape and comparing its features is necessary. This paper aims to propose a model and build a computer systemthat can recognize Bangla Sign Lanugage alphabets and translate them to corresponding Bangla letters by means of deep convolutional neural network (CNN). CNN has been introduced in this model in form of a pre-trained model called “MobileNet” which produced an average accuracy of 95.71% in recognizing 36 Bangla Sign Language alphabets.
Bangla handwritten character recognition using MobileNet V1 architecture Tapotosh Ghosh; Md. Min-Ha-Zul Abedin; Shayer Mahmud Chowdhury; Zarin Tasnim; Tajbia Karim; S. M. Salim Reza; Sabrina Saika; Mohammad Abu Yousuf
Bulletin of Electrical Engineering and Informatics Vol 9, No 6: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v9i6.2234

Abstract

Handwritten character recognition is a very tough task in case of complex shaped alphabet set like Bangla script. As optical character recognition (OCR) has a huge application in mobile devices, model needs to be suitable for mobile applications. Many researches have been performed in this arena but none of them achieved satisfactory accuracy or could not detect more than 200 characters. MobileNet is a state of art (convolutional neural network) CNN architecture which is designed for mobile devices as it requires less computing power. In this paper, we used MobileNet for handwritten character recognition. It has achieved 96.46% accuracy in recognizing 231 classes (171 compound, 50 basic and 10 numerals), 96.17% accuracy in 171 compound character classes, 98.37% accuracy in 50 basic character classes and 99.56% accuracy in 10 numeral character classes. 
Co-Authors A. S. M. Siamuzzaman Shaon Kazi Tahmid Rashad Niloy Md. Min-Ha-Zul Abedin Mohammad Abu Yousuf S. M. Salim Reza S. M. Salim Reza Sabrina Saika Shayer Mahmud Chowdhury Tapotosh Ghosh Tasmima Noushiba Mahbub Tazkia Mim Angona Zarin Tasnim