Recognition of American sign language using LBG vector quantization

Raut, K.; Mali, Shital; Thepade, Sudeep D.; Sanas, Shrikant P.

doi:10.1109/iccci.2014.6921745

Cited by 8 publications

(1 citation statement)

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“…They used k-NN and SVM to classify the 26 letters of the English alphabet in ASL derived from the sensory data. Based on shape and texture characteristics, the LBG (Linde-Buzo-Gray) vector quantization was applied to solve the SLR system [16]. However, the author used RGB information for recognition of sign language in poor lighting; therefore, these methods failed.…”

Section: Related Workmentioning

confidence: 99%

Non-Touch Sign Word Recognition Based on Dynamic Hand Gesture Using Hybrid Segmentation and CNN Feature Fusion

2019

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Hand gesture-based sign language recognition is a prosperous application of human– computer interaction (HCI), where the deaf community, hard of hearing, and deaf family members communicate with the help of a computer device. To help the deaf community, this paper presents a non-touch sign word recognition system that translates the gesture of a sign word into text. However, the uncontrolled environment, perspective light diversity, and partial occlusion may greatly affect the reliability of hand gesture recognition. From this point of view, a hybrid segmentation technique including YCbCr and SkinMask segmentation is developed to identify the hand and extract the feature using the feature fusion of the convolutional neural network (CNN). YCbCr performs image conversion, binarization, erosion, and eventually filling the hole to obtain the segmented images. SkinMask images are obtained by matching the color of the hand. Finally, a multiclass SVM classifier is used to classify the hand gestures of a sign word. As a result, the sign of twenty common words is evaluated in real time, and the test results confirm that this system can not only obtain better-segmented images but also has a higher recognition rate than the conventional ones.

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Section: Related Workmentioning

confidence: 99%