Thai finger-spelling sign language recognition using global and local features with SVM

Pariwat, Thongpan; Seresangtakul, Pusadee

doi:10.1109/kst.2017.7886111

Cited by 25 publications

(14 citation statements)

References 14 publications

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“…Various sign language studies aimed to develop a vision base by using, e.g., image and video processing, object detection, image segmentation, and recognition systems. Sign language recognition systems using vision can also be divided into two types of backgrounds: simple backgrounds [3,4,9,16] and complex backgrounds [17][18][19]. A simple background entails the use of a single color such as green, blue, or white.…”

Section: Review Of Related Literaturementioning

confidence: 99%

“…This can help hand segmentation work more easily, and the system can recognize accuracy at a high level. Pariwat et al [4] used sign language images on a blue background while the signer wore a black jacket in a TFSL recognition system with SVM on RBF, including global and local features. The average accuracy was 91.20%.…”

Section: Review Of Related Literaturementioning

confidence: 99%

“…The average accuracy was 91.20%. Pariwat et al [3] also used a simple background in a system that was developed by combining PHOG and local features with KNN. This combination enhanced the average accuracy to levels as high as 97.80%.…”

Section: Review Of Related Literaturementioning

confidence: 99%

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Multi-Stroke Thai Finger-Spelling Sign Language Recognition System with Deep Learning

Pariwat

Seresangtakul

2021

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Sign language is a type of language for the hearing impaired that people in the general public commonly do not understand. A sign language recognition system, therefore, represents an intermediary between the two sides. As a communication tool, a multi-stroke Thai finger-spelling sign language (TFSL) recognition system featuring deep learning was developed in this study. This research uses a vision-based technique on a complex background with semantic segmentation performed with dilated convolution for hand segmentation, hand strokes separated using optical flow, and learning feature and classification done with convolution neural network (CNN). We then compared the five CNN structures that define the formats. The first format was used to set the number of filters to 64 and the size of the filter to 3 × 3 with 7 layers; the second format used 128 filters, each filter 3 × 3 in size with 7 layers; the third format used the number of filters in ascending order with 7 layers, all of which had an equal 3 × 3 filter size; the fourth format determined the number of filters in ascending order and the size of the filter based on a small size with 7 layers; the final format was a structure based on AlexNet. As a result, the average accuracy was 88.83%, 87.97%, 89.91%, 90.43%, and 92.03%, respectively. We implemented the CNN structure based on AlexNet to create models for multi-stroke TFSL recognition systems. The experiment was performed using an isolated video of 42 Thai alphabets, which are divided into three categories consisting of one stroke, two strokes, and three strokes. The results presented an 88.00% average accuracy for one stroke, 85.42% for two strokes, and 75.00% for three strokes.

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Section: Review Of Related Literaturementioning

confidence: 99%

Section: Review Of Related Literaturementioning

confidence: 99%

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Multi-Stroke Thai Finger-Spelling Sign Language Recognition System with Deep Learning

Pariwat

Seresangtakul

2021

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“…Pada penelitian yang dilakukan [6] jenis Sign language yang digunakan yaitu Japan Sign Language. Selain itu Thai Sign Language juga menjadi objek penelitian seperti yang dilakukan oleh [7]. Jenis sign language yang digunakan di Indonesia dilakukan oleh [2].…”

Section: Pendahuluanunclassified

Pengenalan Alfabet American Sign Language Menggunakan K-Nearest Neighbors Dengan Ekstraksi Fitur Histogram Of Oriented Gradients

Rivan¹,

Irsyad²,

Kevin³

et al. 2020

JuTISI

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Sign Language use to communicate to people with dissabilities. American Sign Language (ASL) one of popular sign language. Histogram of Oriented Gradient (HOG) can be use as feature extraction. Then feature stored in database. K-Nearest Neighbor use to measure distance between feature train and feature test. There are three distance use in this paper consist of Euclidean Distance, Manhattan Distance and Chebychev Distance. The best result are 0,99 when using Euclidean Distance and Manhattan Distance with k=3 dan k=5

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“…Most literature on signal language recognition deal with the relative configuration of the hands [Escobedo-Cardenas and Camara-Chavez 2015], [Almeida et al 2014], [de Paula Neto et al 2015], [Pariwat and Seresangtakul 2017], [Uddin and Chowdhury 2016]. Most of these studies carry out recognition of the alphabet in their respective languages.…”

Section: Related Workmentioning

confidence: 99%

Facial Expression Analysis in Brazilian Sign Language for Sign Recognition

Guerra¹,

Rezende²,

Guimarães³

et al. 2018

Anais Do XV Encontro Nacional De Inteligência Artificial E Computacional (ENIAC 2018)

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Sign language is one of the main forms of communication used by the deaf community. The language’s smallest unit, a “sign”, comprises a series of intricate manual and facial gestures. As opposed to speech recognition, sign language recognition (SLR) lags behind, presenting a multitude of open challenges because this language is visual-motor. This paper aims to explore two novel approaches in feature extraction of facial expressions in SLR, and to propose the use of Random Forest (RF) in Brazilian SLR as a scalable alternative to Support Vector Machines (SVM) and k-Nearest Neighbors (k-NN). Results show that RF’s performance is at least comparable to SVM’s and k-NN’s, and validate non-manual parameter recognition as a consistent step towards SLR.

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Thai finger-spelling sign language recognition using global and local features with SVM

Cited by 25 publications

References 14 publications

Multi-Stroke Thai Finger-Spelling Sign Language Recognition System with Deep Learning

Multi-Stroke Thai Finger-Spelling Sign Language Recognition System with Deep Learning

Pengenalan Alfabet American Sign Language Menggunakan K-Nearest Neighbors Dengan Ekstraksi Fitur Histogram Of Oriented Gradients

Facial Expression Analysis in Brazilian Sign Language for Sign Recognition

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