• The Journal of the Korea institute of electronic communication sciences
• /
• v.15 no.2
• /
• pp.291-298
• /
• 2020

Sign language was designed for the deaf and dumb people to allow them to communicate with others and connect to the society. However, sign language is uncommon to the rest of the society. The unresolved communication barrier had eventually isolated deaf and dumb people from the society. Hence, this study focused on design and implementation of a wearable sign language interpreter. 6 inertial measurement unit (IMU) were placed on back of hand palm and each fingertips to capture hand and finger movements and orientations. Total of 28 proposed word-based American Sign Language were collected during the experiment, while 156 features were extracted from the collected data for classification. With the used of the long short-term memory (LSTM) algorithm, this system achieved up to 99.89% of accuracy. The high accuracy system performance indicated that this proposed system has a great potential to serve the deaf and dumb communities and resolve the communication gap.

• Journal of Computing Science and Engineering
• /
• v.10 no.3
• /
• pp.95-101
• /
• 2016

In this study, we propose a new system for constructing parallel corpora for sign languages, which are generally under-resourced in comparison to spoken languages. In order to achieve scalability and accessibility regarding data collection and corpus construction, our system utilizes deep learning-based techniques and predicts depth information to perform pose estimation on hand information obtainable from video recordings by a single RGB camera. These estimated poses are then transcribed into expressions in SignWriting. We evaluate the accuracy of hand tracking and hand pose estimation modules of our system quantitatively, using the American Sign Language Image Dataset and the American Sign Language Lexicon Video Dataset. The evaluation results show that our transcription system has a high potential to be successfully employed in constructing a sizable sign language corpus using various types of video resources.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.18 no.3
• /
• pp.449-458
• /
• 2023

Many people think that sign language is only for people who are deaf and cannot speak, but of course it is necessary for people who want to talk with them. One of the biggest challenges in ASL(American Sign Language) alphabet recognition is the high inter-class similarities and high intra-class variance. In this paper, we proposed an architecture that can overcome these two problems, which performs similarity learning to reduces inter-class similarities and intra-class variance between images. The proposed architecture consists of the same convolutional neural network with a double configuration that shares parameters (weights and biases) and also applies the Keras API to reduce similarity learning and variance through this pathway. The similarity learning results the use of the dual CNN shows that the accuracy is improved by reducing the similarity and variability between classes by not including the poor results of the two classes.

• Journal of KIISE:Software and Applications
• /
• v.36 no.12
• /
• pp.1034-1037
• /
• 2009

Sign language spotting is the task of detecting the start and end points of signs from continuous data and recognizing the detected signs in the predefined vocabulary. The difficulty with sign language spotting is that instances of signs vary in both motion and shape. Moreover, signs have variable motion in terms of both trajectory and length. Especially, variable sign lengths result in problems with spotting signs in a video sequence, because short signs involve less information and fewer changes than long signs. In this paper, we propose a method for spotting variable lengths signs based on semi-CRF (semi-Markov Conditional Random Field). We performed experiments with ASL (American Sign Language) and KSL (Korean Sign Language) dataset of continuous sign sentences to demonstrate the efficiency of the proposed method. Experimental results show that the proposed method outperforms both HMM and CRF.

• Journal of rehabilitation welfare engineering & assistive technology
• /
• v.10 no.4
• /
• pp.329-336
• /
• 2016

Deaf people using sign language are experiencing social inequalities and financial losses due to communication restrictions. In this paper, real-time pattern recognition algorithm was applied to distinguish American Sign Language using an armband sensor(8-channel EMG sensors and one IMU) to enable communication between the deaf and the hearing people. The validation test was carried out with 11 people. Learning pattern classifier was established by gradually increasing the number of training database. Results showed that the recognition accuracy was over 97% with 20 training samples and over 99% with 30 training samples. The present study shows that sign language recognition using armband sensor is more convenient and well-performed.

• Journal of KIISE
• /
• v.44 no.2
• /
• pp.163-170
• /
• 2017

Despite the rise of studies in spoken to sign language translation, low-resource problems of sign language corpus have been rarely addressed. As a first step towards translating from spoken to sign language, we addressed the problems arising from resource scarcity when translating spoken language to manual signals translation using statistical machine translation techniques. More specifically, we proposed three preprocessing methods: 1) paraphrase generation, which increases the size of the corpora, 2) lemmatization, which increases the frequency of each word in the corpora and the translatability of new input words in spoken language, and 3) elimination of function words that are not glossed into manual signals, which match the corresponding constituents of the bilingual sentence pairs. In our experiments, we used different types of English-American sign language parallel corpora. The experimental results showed that the system with each method and the combination of the methods improved the quality of manual signals translation, regardless of the type of the corpora.

• Journal of Institute of Control, Robotics and Systems
• /
• v.20 no.6
• /
• pp.593-598
• /
• 2014

Hand gesture recognition based on vision is a challenging task in human-robot interaction. The sign language of finger spelling alphabets has been tested as a kind of hand gesture. In this paper, we test hand gesture recognition by detecting the contour shape and orientation of hand with visual image. The method has three stages, the first stage of finding hand component separated from the background image, the second stage of extracting the contour feature over the hand component and the last stage of comparing the feature with the reference features in the database. Here, finger spelling alphabets are used to verify the performance of our system and our method shows good performance to discriminate finger alphabets.

• Journal of English Language & Literature
• /
• v.54 no.4
• /
• pp.561-577
• /
• 2008

L.M. Montgomery's Anne of Green Gables is not only confidently labelled a Canadian classic but also placed as a national icon along with the moose, the beaver, and the Habs in Canada. Anne's 'Canadianness' is partly due to its location in the rural world of Prince Edward Island. The fictional Avonlea is described as the ideal space where Canadian spirit can interact with the personified surrounding landscapes through Celtic imagination. Additionally, the communal bond of Avonlea fully demonstrates Scottish Canadian identities. The Scottish national character of Avonlea is responsible for clannishness of the Cuthberts and the Lyndes. The disrespect to the French is also due to Scottish heritage in Avonlea. As an outsider Anne wants to be integrated into the community of Avonlea, and successfully adapts herself to the regional shared values. Meanwhile she partly challenges the strictness and rigidness of the born Canadian Avonlea residents. Despite its Canadian origin, Anne of Green Gables is accepted as part of the American canon of children's literature in the Unite States. The configuration of Anne as an American heroine is noticeable among American scholars: by relocating it to the US the female Bildungsroman in the nineteenth century America, a group of literary critics adapt Anne as an American girl for American readers. The heroine of Anne of Green Gables is linked to American novels such as Louisa May Alcott's Little Women, Kate Douglas Wiggin's Rebecca of Sunnybrook Farm and Gene Stratten Porter's A Girl of the Limberlost. Anne is even classified as another Caddie by American literary critics: Anne is placed at the center of Caddie Woodlawn Syndrome as another Wisconsin pioneer child. Canadian identity of Anne is intentionally excluded and Anne was reborn as an American girl in the U.S. In this context, Anne functions as a sign of nation and a site for cross-national identity formation.

• ETRI Journal
• /
• v.45 no.6
• /
• pp.1022-1034
• /
• 2023

In this study, the impact of varying lighting conditions on recognition and decision-making was considered. The luminosity approach was presented to increase gesture recognition performance under varied lighting. An efficient framework was proposed for sensor-based sign language gesture identification, including picture acquisition, preparing data, obtaining features, and recognition. The depth images were collected using multiple Microsoft Kinect devices, and data were acquired by varying resolutions to demonstrate the idea. A case study was designed to attain acceptable accuracy in gesture recognition under variant lighting. Using American Sign Language (ASL), the dataset was created and analyzed under various lighting conditions. In ASL-based images, significant feature points were selected using the scale-invariant feature transformation (SIFT). Finally, an artificial neural network (ANN) classified hand gestures using specified characteristics for validation. The suggested method was successful across a variety of illumination conditions and different image sizes. The total effectiveness of NN architecture was shown by the 97.6% recognition accuracy rate of 26 alphabets dataset with just a 2.4% error rate.

• English Language & Literature Teaching
• /
• v.17 no.2
• /
• pp.1-18
• /
• 2011

The object of this study is to show some features of English, focused on such mechanics as punctuation and transition, in Korean presidential addresses transcribed in English which are different from those of the United States. Towards that end, the presidential addresses of the United States and Korea from January, 2010 to June, 2010 are collected, made into corpora, and analyzed. Through analyzing the corpora, this paper is to address the following research questions: (1) What features can be regarded as different in terms of punctuation and transition? (2) If there are any differences between the corpora, are they significant enough to pose any problems for Korean and American English users to communicate with each other? (3) If so, what can be done to solve the problems in regard to pedagogical implications? Overall, as for punctuation, both Presidents' addresses share a lot in common, even with some idiosyncratic variations though. However, there are some noticeable differences in transitional devices. It is not clear whether those should be taken as a sign of personal preference, though. Transitional markers are meant to be part of wording in writing. (196 words).