Thai Sign Language Image Recognition for the Hearing-Impaired using Radial Inverse Force Histogram combined with Max-Min Boundary
Nattapong Songneam

Nattapong Songneam*, Department of Computer Science, Faculty of Science and Technology, Phranakhon Rajaphat University, Bangkok, Thailand. Email: 
Manuscript received on June 19, 2021. | Revised Manuscript received on June 25, 2021. | Manuscript published on June 30, 2021. | PP: 422-433 | Volume-10 Issue-5, June 2021. | Retrieval Number: 100.1/ijeat.E28680610521 | DOI: 10.35940/ijeat.E2756.0610521
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Abstract: Sign language image recognition is also a very interesting research topic. Because it can be applied to help normal people understand and use it as a communication tool for the hearing impaired. The objectives of this research were to: 1) study and analyze Thai Sign Language image recognition image data for hearing impaired, 2) develop Thai Sign Language image recognition system for hearing impaired by using new techniques, and 3) measure the efficiency of Thai sign language image recognition for hearing impaired using Radial Inverse Force Histogram and the Maximum and Minimum boundary values. The results of the research were as follows: 1) The study and analysis of image data of Thai Sign Language Image Recognition In this research, 62,694 sign language images were used, divided into 2 parts: 1) American Sign Language images, which consisted of 36 groups of images, namely 26 groups of letters (AZ) and 10 groups of numbers (0-9), and Part 2) Picture of Thai Sign Language consisting of 61 groups of images, including 44 groups of letters (ก-ฮ), 7 groups of vowels and 10 groups of numbers (0-9). Each group of pictures is rotated, enlarged, and Image promotion There were 6 sub-groups of images in various forms, divided into 2 parts: 70% of the images for training and 30% of the images for testing. 3) The results of measuring the efficiency of image recognition. It is divided into two parts: American Sign Language Image Recognition and Thai Sign Language Image Recognition. Compared with the Angular histogram method, the mean image accuracy was 0.86, the recall of the mean American sign language was 0.91, and the accuracy of the Thai Sign Language was 0.78. The recognition performance for Thai Sign Language images averaged 0.89, while the recognition efficiency was achieved when using radial inverse force histograms in combination with image similarity measurements with maximum-minimum boundary values. Accuracy for Mean American Sign Language was 0.99 and Remembrance for Mean American Sign was 1.00, while Accuracy for Mean Thai Sign Language was 0.89. Mean Remembrance for Thai Sign Language was 0.96. The results of the visual recognition performance measurement of both the American Sign Language and the Thai Sign Language images were very good compared to the Angular Histogram method. 
Keywords: Image Recognition, Thai Sign Language, American Sign Language, Hearing-Impaired, Radial Inverse Force Histogram, Maximum and Minimum Boundary Values
Scope of the Article: Image Processing and Pattern Recognition