User-independent system for sign language finger spelling recognition

Kishore

Mathematical Problems in Engineering

2017

Extracting and recognizing complex human movements from unconstraint online video sequence is an interesting task. In this paper the complicated problem from the class is approached using unconstraint video sequences belonging to Indian classical dance forms. A new segmentation model is developed using discrete wavelet transform and local binary pattern (LBP) features for segmentation. A 2D point cloud is created from the local human shape changes in subsequent video frames. The classifier is fed with 5 types of features calculated from Zernike moments, Hu moments, shape signature, LBP features, and Haar features. We also explore multiple feature fusion models with early fusion during segmentation stage and late fusion after segmentation for improving the classification process. The extracted features input the Adaboost multiclass classifier with labels from the corresponding song (tala). We test the classifier on online dance videos and on an Indian classical dance dataset prepared in our lab. The algorithms were tested for accuracy and correctness in identifying the dance postures.

“…The normalized values in the confusion matrix are calculated using (26). We did plot the confusion matrix for the song sequence "Siva Shamboo" with first 28 word labels.…”

Section: Experimentation and Resultsmentioning

confidence: 99%

Section: Experimentation and Resultsmentioning

confidence: 99%

Section: Experimentation and Resultsmentioning

confidence: 99%

Section: Literature Surveymentioning

confidence: 99%

See 2 more Smart Citations

Indian Classical Dance Classification with Adaboost Multiclass Classifier on Multifeature Fusion

Kishore

Mathematical Problems in Engineering

2017

“…More recently, geometric and orthogonal moments for view independent gesture classification were introduced in [39]. The Tchebichef moments computed from internal and external hand edges for hand shape representation and hand posture recognition was reported in [15]. Kernel principal component analysis (KPCA) and non-parametric discriminant analysis (NDA) are applied to recognize Australian Sign Language and use KPCA to detect discriminated features from the KPCA feature space can be found in [36].…”

Section: Related Workmentioning

confidence: 99%

Sign Energy Images for Recognition of Sign Language at Sentence Level

Kumara¹,

Nagendraswamy²

2016

IJCA

In this paper, the task of sign language recognition at sentence level is addressed. The idea of Sign Energy Image (SEI) and a method of extracting Fuzzy-Gaussian Local Binary Pattern (FzGLBP) features from SEI to characterize the sign are explored. The suitability of interval valued type symbolic data for efficient representation of signs in the knowledgebase is studied. A Chi-square proximity measure is used to establish matching between reference and test signs. A simple nearest neighbor classification technique is used for recognizing signs. Extensive experiments are conducted to study the efficacy of the proposed system. A data base of signs called UoM-ISL is created for experimental analysis.

Concurrency and Computation

A review on computational methods based automated sign language recognition system for hearing and speech impaired community

Robert

Hemanth

2023

Summary The recent advancements in computer vision and deep learning have led to promising progress in various motion detection and gesture recognition methods. Thriving efforts in the field of sign language recognition (SLR) during recent years led to interaction between humans and computer systems. Contributing a real‐time automated Sign Language recognition system will be a remarkable treasure for hearing and speech‐impaired people that will break the barriers of interaction with the real world. Albeit several research works are accomplished in sign language recognition, there is still a demand for developing a real‐time automated sign language recognition system. Compared to other methodologies, the techniques adopted may have advantages and disadvantages, and they vary from researcher to researcher. There are still some issues with employing these SLR models and procedures regularly, even though numerous research studies have been conducted to determine the most acceptable methods and models for sign language recognition. It gets more expensive and difficult in terms of resources when the developed automated SLR system becomes available as a product. Hence, for the welfare of the hearing and speech‐impaired community, the researchers are still endeavoring way to find a cost‐effective method. This work brings forth the challenges faced by scientists in developing a cost‐effective commercial prototype for the hearing and speech‐impaired community. This paper also explores and analyses various deep learning techniques and methods used in developing a sign language recognition system. The objective behind this work is to identify the best method which produces high accuracy in developing a cost‐effective sign language recognition system aiding communication between signers and non‐signers so that they can be a part of growing technology.