Novel Technique for Isolated Sign Language Based on Fingerspelling Recognition Sign language is used by deaf and hard hearing people to exchange information between their own community and with other people. Fingerspelling recognition method from isolate sign language has attracted research interest in computer vision and humancomputer interaction based on a novel technique. The essential for realtime recognition of isolate sign language has grown with the emergence of bettercapturing devices such as Kinect sensors. The purpose of this paper is to design a user independent framework for automatic recognition of American Sign Language which can recognize several onehanded dynamic isolated signs and interpreting their meaning. We built datasets as a raw data for alphabets (Axe2x80x93Z) or numbers (1xe2x80x9320) by used lefthand the 3D point (X L , Y L , Z L ) or switch by righthand (X R , Y R , Z R ) centroid as one of contribution. The proposed approach was tested for gestures that involve lefthand or righthand and was compared with other approach and gave better accuracy. Two machine learning methods are involved like Hidden Conditional Random Field (HCRF), and Random Decision Forest (RDF) for the classification part. The third contribution based on low lighting condition and cluttered background. In this research work is achieved for recognition accuracy over 99.7%.

Sign Language Recognition Analysis using Multimodal Data Voicecontrolled personal and home assistants (such as the Amazon Echo and Apple Siri) are becoming increasingly popular for a variety of applications. However, the benefits of these technologies are not readily accessible to Deaf or HardofHearing (DHH) users. The objective of this study is to develop and evaluate a sign recognition system using multiple modalities that can be used by DHH signers to interact with voicecontrolled devices. With the advancement of depth sensors, skeletal data is used for applications like video analysis and activity recognition. Despite having similarity with the wellstudied human activity recognition, the use of 3D skeleton data in sign language recognition is rare. This is because unlike activity recognition, sign language is mostly dependent on hand shape pattern. In this work, we investigate the feasibility of using skeletal and RGB video data for sign language recognition using a combination of different deep learning architectures. We validate our results on a largescale American Sign Language (ASL) dataset of 12 users and 13107 samples across 51 signs. It is named as GMUASL51. 1 We collected the dataset over 6 months and it will be publicly released in the hope of spurring further machine learning research towards providing improved accessibility for digital assistants.