Indian sign language recognition: An approach based on fuzzy-symbolic data

Nagendraswamy, H. S.; Kumara, B M Chethana; Chinmayi, R

doi:10.1109/icacci.2016.7732176

Cited by 14 publications

(4 citation statements)

References 20 publications

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“…Pattern identification result of BISINDO alphabets is not uniform or heterogeneous. Some scientific studies about the identification of hand shape that representing a letter [3], [4], [5], [6].…”

Section: Introductionmentioning

confidence: 99%

Recognition of Bisindo Alphabets Based on Chain Code Contour and Similarity of Euclidean Distance

Indra

Madenda

Wibowo

2017

International Journal on Advanced Science, Engineering and Information Technology

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In Indonesia, there are two forms of sign language practiced in the community, i.e., Indonesian sign language or known as BISINDO, and Indonesian sign language system or known as SIBI. In this study, we conduct research about recognition of Bisindo alphabets using contour chain code for the method of feature extraction and similarity of Euclidean distance for the method of recognition. The features used are the probability of chain code generated from contour following and the formation of chain code. The proposed method in this study consisted of five section, i.e., input test image, segmentation, edge detection, feature extraction and matching process of the alphabet. In the testing of the proposed method, we used 52 images of hand gestures used as test images. The images are in the form of static images and 26 images of hand gestures used as reference images which represent 26 alphabets BISINDO from A to Z, where the images stored in the database. The test images of different shapes and sizes with image references. For recognition, we do the matching between the probability of the test image chain code with the probability of the reference image chain code using Euclidean distance. The measurement result of Euclidean distance in this study was generated average accuracy rate of similarity above 94%. This indicates that the method proposed in this study was effective and produce the level of similarity of BISINDO alphabets was accurate

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Section: Introductionmentioning

confidence: 99%

Recognition of Bisindo Alphabets Based on Chain Code Contour and Similarity of Euclidean Distance

Indra

Madenda

Wibowo

2017

International Journal on Advanced Science, Engineering and Information Technology

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“…Distinctive orientation containers are fixed, and a rectangular candidate sub window is given with the gradient magnitude of every pixel allotted to an orientation container which is nearest to gradient orientation of the pixel. It ends up both simple and quick to register with the utilization of one basic image per orientation container to compute the gradient magnitude summation for any applicant sub window [9]. EOH has been used for detecting sign boundaries.…”

Section: ) Edge Oriented Histogrammentioning

confidence: 99%

“…Edge Oriented Histogram (EOH) [9] It is unique feature which represents frequency and directionality of the brightness variations.…”

Section: Histogram Of Oriented Gradients (Hog) [12]mentioning

confidence: 99%

Feature extraction strategies for Indian Sign Language Recognition: A Review

Patel¹

2018

IJRASET

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Sign language is the language used by the general population with hearing and talking disabilities for communication in their day to day life. There are many sign languages such as Indian Sign Language (ISL), American Sign Language (ASL), and Chinese Sign Language (CSL), British Sign Language BSL). More research work is done in ASL side than ISL because ASL uses a single hand for making signs. But ISL is a language which is double-handed sign language so it becomes quite difficult for making recognition system for it. By couple of years researchers from India try to develop ISL recognition systems. There are mainly four stages associated with sign language recognition -image acquisition, preprocessing, feature extraction and classification.

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“…Hand-shape recognition has been achieved using handcrafted features and techniques including Hidden Markov Models, Fourier Analysis, Points of Interest, Principal Component Analysis (PCA), Orientation Histograms and Kalman Filters [14], [9]. In [12], a PCA-based method has been presented.…”

Section: Related Workmentioning

confidence: 99%

Irish Sign Language Recognition Using Principal Component Analysis and Convolutional Neural Networks

Oliveira

Chatbri

Little

et al. 2017

2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA)

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Abstract-Hand-shape recognition is an important problem in computer vision with significant societal impact. In this work, we introduce a new image dataset for Irish Sign Language (ISL) recognition and we compare between two recognition approaches. The dataset was collected by filming human subjects performing ISL hand-shapes and movements. Then, we extracted frames from the videos. This produced a total of 52,688 images for the 23 common hand-shapes from ISL. Afterwards, we filter the redundant images with an iterative image selection process that selects the images which keep the dataset diverse. For classification, we use Principal Component Analysis (PCA) with with K-Nearest Neighbours (k-NN) and Convolutional Neural Networks (CNN). We obtain a recognition accuracy of 0.95 for our PCA model and 0.99 for our CNN model. We show that image blurring improves PCA results to 0.98. In addition, we compare times for classification.

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Indian sign language recognition: An approach based on fuzzy-symbolic data

Cited by 14 publications

References 20 publications

Recognition of Bisindo Alphabets Based on Chain Code Contour and Similarity of Euclidean Distance

Recognition of Bisindo Alphabets Based on Chain Code Contour and Similarity of Euclidean Distance

Feature extraction strategies for Indian Sign Language Recognition: A Review

Irish Sign Language Recognition Using Principal Component Analysis and Convolutional Neural Networks

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