Sign Language is one of the media of communication for deaf people. One should learn sign language to interact with them. Learning usually takes place in peer groups. There exist very few study materials for sign learning. Because of this, the process of learning sign language learning is a difficult task. Fingerspelled sign learning is the initial stage of sign learning and moreover, are used when no corresponding sign exists or signer is not aware of it. Most of the existing tools for sign language learning use external sensors which are costly. This paper discusses SignQuiz, which is a cost-effective web-based fingerspelled sign learning application for Indian sign language (ISL) utilizing automatic sign language recognition technique. SignQuiz helps to learn signs without any external help. This is the first attempt in ISL for learning finger spelled signs using a deep neural network. The results indicate that SignQuiz is better than the printed medium for fingerspelled sign learning. INDEX TERMS Assistive technology, sign language, learning technology.

Object Detection and Tracking in SurveillanceSystem is inevitable in the present scenario, as it is not possible for a person to continuously monitor the video clips in real time. We propose an efficient and novel system for detecting moving objects in a surveillance video and predict whether it is a human or not. In order to account for faster object detection, we use an established Background Subtraction Algorithm known as Mixture of Gaussians. A set of simple and efficient features are extracted and provided to Support Vector Machine. The performance of the system is evaluated with different kernels of SVM and also for K Nearest Neighbor Classifier with its various distance metrics. The system is evaluated using statistical measurements, and the experiments resulted in average F measure of 86.925% and thus prove the efficiency of the novel system.

This paper presents an efficient Online Handwritten character Recognition System for Malayalam Characters (OHR-M) using Kohonen network. It would help in recognizing Malayalam text entered using pen-like devices. It will be more natural and efficient way for users to enter text using a pen than keyboard and mouse. To identify the difference between similar characters in Malayalam a novel feature extraction method has been adopted-a combination of context bitmap and normalized (x, y) coordinates. The system reported an accuracy of 88.75% which is writer independent with a recognition time of 15-32 milliseconds.

This paper presents a Robust Content Based Video Retrieval (CBVR) system. This system retrieves similar videos based on a local feature descriptor called SURF (Speeded Up Robust Feature). The higher dimensionality of SURF like feature descriptors causes huge storage consumption during indexing of video information. To achieve a dimensionality reduction on the SURF feature descriptor, this system employs a stochastic dimensionality reduction method and thus provides a model data for the videos. On retrieval, the model data of the test clip is classified to its similar videos using a minimum distance classifier. The performance of this system is evaluated using two different minimum distance classifiers during the retrieval stage. The experimental analyses performed on the system shows that the system has a retrieval performance of 78%. This system also analyses the performance efficiency of the low dimensional SURF descriptor.

On-line handwriting recognition has been a frontier area of research for the last few decades under the purview of pattern recognition. Word processing turns to be a vexing experience even if it is with the assistance of an alphanumeric keyboard in Indian languages. A natural solution for this problem is offered through online character recognition. There is abundant literature on the handwriting recognition of western, Chinese and Japanese scripts, but there are very few related to the recognition of Indic script such as Malayalam. This paper presents an efficient Online Handwritten character Recognition System for Malayalam Characters (OHR-M) using K-NN algorithm. It would help in recognizing Malayalam text entered using pen-like devices. A novel feature extraction method, a combination of time domain features and dynamic representation of writing direction along with its curvature is used for recognizing Malayalam characters. This writer independent system gives an excellent accuracy of 98.125% with recognition time of 15-30 milliseconds.