Tamil Sign Research Articles

Tamil Sign Language Translation and Recognition System for Deaf-mute People Using Image Processing Techniques

This work concentrates on the device, which helps as a translation system for translating sign gestures into text. The disabled people, in particular, hearing and speech impaired people, are facing difficulties in society. Communication of disabled people becomes worse as the majority of ordinary people do not understand it. These disabled people face difficulty communicating with others; some have many problems communicating with others in sign language. It causes the communication gap between them where impaired people cannot share their views and skills with others. We headed to facilitate communication between the disabled people and the "Tamil sign language translator to solve this problem." Here, gestures are translated to Tamil language to find a localized solution. It processes 31 Tamil alphabets, 12 Vowels, 18 Consonants, and 1 Aayudha Ezhuthu. It is 32 combinations with five fingers points either up or down and mapped to decimal numbers. Here in this process, we need edge detection, which is accurately done and Processed by canny edge detection. In addition to this process, we have used two gesture recognition methods and training the input system through our mainframe algorithm called scale-invariant feature detection transform. We have developed this system, which is useful for deaf and dumb people for essential communication.

TAMIL SIGN LANGUAGE RECOGNITION IN REAL TIME

Communication is interchanging of ideas information or message from one person to the other person, sign language is made with the hand and other movement, including facial expressions and posture of the body used by the people of unable to speak and hearing, there are different type of sign language. Tamil sign language is the regional sign language, the aim of the work is provide the real time recognition of Tamil sign Language (TSL) in to Tamil letter, here we introduced the convolutional neural network (CNN) as a classifier used as training the Tamil sign language and predict the Tamil sign language, the process are divided in two section one is to train by using the keras model and the second section is the skin segmentation of the hand gestures in the region of the interest, there are two phase in training the model, having set of the hand gestures images in the training set and testing set by using the image train the model, the model occurred the 100% accuracy in the white background and good lighting condition, 97.5 % in the low lighting condition.

SYNERGISTIC FIBROBLAST OPTIMIZATION BASED BOUNDARY DETECTION IN TAMIL SIGN LANGUAGE IMAGES

Sign Language (SL) is a three dimensional language used for communication by deaf people. The recognition system for SL is always an apprehensive task which is handled by vision collaboration and technology. Basically, detection of edges is deliberated to be the precursor for detection of objects, as the edges are the outline of the objects. Detecting continuous edges in real time images is a hard problem, especially in Tamil Sign Language (TSL) recognition system. This paper proposes an algorithm which finds optimal threshold values (L and H) based on Synergistic Fibroblast Optimization (SFO) for detection of continuous, smooth and thin edges of TSL hand pose images. A novel SFO algorithm is proposed with sphere objective function and two constraints for reducing the ruined edges. The efficiency of the algorithm is compared experimentally with conventional Canny, Classical PSO and variant based PSO on TSL Consonants images. Experimental results suggested that the novel SFO based canny operator detects edges more accurately, and the edges detected are smoother and thinner when compared to other analyzed algorithms.

PSO-based Optimized Canny Technique for Efficient Boundary Detection in Tamil Sign Language Digital Images

For the hearing impaired, sign language is the most prevailing means of communication for their day-to-day life. It is always a challenge to develop an optimized automated system to recognize and interpret the implication of signs expressed by the hearing impaired. There are a wide range of algorithms developed for SLR, in which only few considerable approaches are carried out in Tamil Sign Language Recognition. This paper has proposed a significant contribution in segmentation process which is the most predominant component of image analysis in constructing the SLR system. Segmentation is handled using edge detection procedure for finding the borders of hand sign within the captured images, by detecting the split in the image illumination. The objective of the edge function, to find the boundary intensity, is done by a particle swarm optimization technique which chooses the optimal threshold values and implemented in the canny hysteresis thresholding method. The analysis primarily uses common edge recognition algorithms which contain Sobel, Robert, Canny and Prewitt from which the scope of the work is extended by introducing an optimization technique in Canny method. The performance of the proposed algorithm is tested with real time Tamil sign language dataset and comparison is inevitably carried out with standard segmentation metrics

Tamil Sign Language to Speech Translation

Sign Language Recognition is one of the most growing fields of research today. Most researches on hand gesture recognition for HCI rely on either Artificial Neural Networks (ANN) or Hidden Markov Model (HMM). There are many effective algorithms for segmentation, classification, pattern matching and recognition. The main goal of this paper is to compare the classifiers for translating Tamil sign language to speech, which will definitely help the researchers to attain an optimal solution. The most important thing in hand gesture recognition system is the input features and the selection of classifiers. To increase the recognition rate and make the recognition system resilient to view-point variations, the concept of shape descriptors from the available feature set is introduced. K-Nearest Neighbor (KNN), Proximal Support Vector Machine (PSVM) and Naive Bayesian are used as classifiers to recognize static Tamil words. The performance analysis of the proposed approach is presented along with the experimental results. Comparative analysis of these methods with other popular techniques shows that the real time efficiency and robustness are better. Experimental results demonstrate the effectiveness of the proposed work for recognizing efficiency 78% for KNN classifier, 91% for PSVM classifier and 93% for Naive Bayesian classifier.

Design and Development of Tamil Sign Alphabets using Image Processing with Right Hand Palm to Aid Deaf-Dumb People

Hand recognition is a recent active area of research in the computer vision for the purpose of Human - Computer Interaction. This paper mainly concentrates on Tamil sign alphabets (TSL) into speech which could be helpful for deaf-dumb people. In this paper, a set of 32 (25) combinations of binary number sign images are introduced to propose a system to recognize Tamil sign alphabets. These Tamil alphabets have 12 vowels, 18 consonants, and one Aayutha Ezhuthu. The proposed system is based on four main stages: Pre-processing method, Training phase, Sign detection, and Conversion of Binary to voice. The binary sign images are loaded at a run time or static as 310 images which are taken ten times in different distances at the same position. The Ave fingertip positions represent (‘1’ or ‘0’) and are identified by using image processing techniques with proposed right hand palm angular-based analysis. Then, the binary values are assigned to the corresponding Tamil letters and voice. The experiments were performed with ten different signer palms and the results demonstrated that the system could successfully recognize Tamil sign alphabets with better accuracy with 99.35% of static images and 98.36% of dynamic images (runtime).

Recognition of the Tamil sign alphabet using image processing technique with angular-based analysis of left hand to aid deaf-dumb people

Human-computer interaction is an important research work for enabling convenient communication between deaf-dumb and normal people. This paper introduces a Tamil sign recognition system for one of south Indian languages. Tamil alphabets have 12 vowels, 18 consonants and one Ayutha Ezhuthu. This proposed method introduces a set of 31 sign combination of binary images. Each image representing five finger positions in the form of binary ‘UP’ and ‘DOWN’ type. The images represent the left hand palm side which is to be taken ten times at different distances as static 320 images. Fingertip position identification of ‘UP’ or ‘DOWN’ is identified by using efficient and fast algorithm image processing techniques. Then a binary type of fingertip position is converted into corresponding Tamil letters. The experiments revealed that the system is able to recognise Tamil sign language with 98.44% better accuracy.

Recognition of Tamil Sign Language Alphabet using Image Processing to aid Deaf-Dumb People

This paper proposes a method that provides the conversion of a set of 32 combination of the binary number 25 which represents the UP and DOWN positions of fiver fingers into decimal numbers. For the conversion of binary numbers, Palm Extraction method, Feature point extraction method, training phase and testing phase are used. The static and dynamic images can be converted by these methods. Then the binary numbers are converted into decimal numbers by using Binary-Decimal conversion algorithm and then the decimal numbers are converted into corresponding Tamil letters 12 vowels, 18 consonants and 1 Aayutha Ezhuthu (▪).