• Journal of the Korea Society of Computer and Information
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• v.24 no.2
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• pp.193-200
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• 2019

Hearing impaired people are living in a voice culture area, but due to the difficulty of communicating with normal people using sign language, many people experience discomfort in daily life and social life and various disadvantages unlike their desires. Therefore, in this paper, we study a sign language translation system for communication between a normal person and a hearing impaired person using sign language and implement a prototype system for this. Previous studies on sign language translation systems for communication between normal people and hearing impaired people using sign language are classified into two types using video image system and shape input device. However, existing sign language translation systems have some problems that they do not recognize various sign language expressions of sign language users and require special devices. In this paper, we use machine learning method of artificial neural network to recognize various sign language expressions of sign language users. By using generalized smart phone and various video equipment for sign language image recognition, we intend to improve the usability of sign language translation system.

• Journal of Korea Multimedia Society
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• v.8 no.6
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• pp.768-775
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• 2005

For sign languages are main communication means among hearing-impaired people, there are communication difficulties between speaking-oriented people and sign-language-oriented people. Automated sign-language recognition may resolve these communication problems. In sign languages, finger spelling is used to spell names and words that are not listed in the dictionary. There have been research activities for gesture and posture recognition using glove-based devices. However, these devices are often expensive, cumbersome, and inadequate for recognizing elaborate finger spelling. Use of colored patches or gloves also cause uneasiness. In this paper, a vision-based finger spelling recognition system is introduced. In our method, captured hand region images were separated from the background using a skin detection algorithm assuming that there are no skin-colored objects in the background. Then, hand postures were recognized using a two-dimensional grid analysis method. Our recognition system is not sensitive to the size or the rotation of the input posture images. By optimizing the weights of the posture features using a genetic algorithm, our system achieved high accuracy that matches other systems using devices or colored gloves. We applied our posture recognition system for detecting Korean Sign Language, achieving better than $93\%$ accuracy.

• International Journal of Computer Science & Network Security
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• v.21 no.3
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• pp.37-40
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• 2021

This paper presents a design of a speech to sign language converter for deaf and hard of hearing people. The device is low-cost, low-power consumption, and it can be able to work entirely offline. The speech recognition is implemented using an open-source API, Pocketsphinx library. In this work, we proposed a context-oriented language model, which measures the similarity between the recognized speech and the predefined speech to decide the output. The output speech is selected from the recommended speech stored in the database, which is the best match to the recognized speech. The proposed context-oriented language model can improve the speech recognition rate by 21% for working entirely offline. A decision module based on determining the similarity between the two texts using Levenshtein distance decides the output sign language. The output sign language corresponding to the recognized speech is generated as a set of sequential images. The speech to sign language converter is deployed on a Raspberry Pi Zero board for low-cost deaf assistive devices.

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

• The Journal of the Korea Contents Association
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• v.5 no.4
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• pp.85-93
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• 2005

In this paper, we propose recognition system which tracks the unmarked hand of a person performing sign language in complex background. First of all, we measure entropy for the difference image between continuous frames. Using a color information that is similar to a skin color in candidate region which has high value, we extract hand region only from background image. On the extracted hand region, we detect a contour and recognize sign language by applying improved centroidal profile method. In the experimental results for 6 kinds of sing language movement, unlike existing methods, we can stably recognize sign language in complex background and illumination changes without marker. Also, it shows the recognition rate with more than 95% for person and $90\sim100%$ for each movement at 15 frames/second.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.105.2-105
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• 2001

This paper studies continuous Korean Sign Language (KSL) recognition using color vision. In recognizing gesture words such as sign language, it is a very difficult to segment a continuous sign into individual sign words since the patterns are very complicated and diverse. To solve this problem, we disassemble the KSL into 18 hand motion classes according to their patterns and represent the sign words as some combination of hand motions. Observing the speed and the change of speed of hand motion and using state automata, we reject unintentional gesture motions such as preparatory motion and meaningless movement between sign words. To recognize 18 hand motion classes we adopt Hidden Markov Model (HMM). Using these methods, we recognize 5 KSL sentences and obtain 94% recognition ratio.

• Proceedings of the Korean Society of Computer Information Conference
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• 2024.01a
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• pp.317-319
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• 2024

본 논문에서는 비디오 캡셔닝 알고리즘을 적용한 수어 번역 및 행동 인식 알고리즘을 적용한 수어 인식 알고리즘을 제안한다. 본 논문에 사용된 비디오 캡셔닝 알고리즘으로 40개의 연속된 입력 데이터 프레임을 CNN 네트워크를 통해 임베딩 하고 트랜스포머의 입력으로 하여 문장을 출력하였다. 행동 인식 알고리즘은 랜덤 샘플링을 하여 한 영상에 40개의 인덱스에서 40개의 연속된 데이터에 CNN 네트워크를 통해 임베딩하고 GRU, 트랜스포머를 결합한 RNN 모델을 통해 인식 결과를 출력하였다. 수어 번역에서 BLEU-4의 경우 7.85, CIDEr는 53.12를 얻었고 수어 인식으로 96.26%의 인식 정확도를 얻었다.

• Journal of the Ergonomics Society of Korea
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• v.34 no.5
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• pp.411-426
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• 2015

Objective: The goal of this thesis is to design the interaction structure and framework of system to recognize sign language. Background: The sign language of meaningful individual gestures is combined to construct a sentence, so it is difficult to interpret and recognize the meaning of hand gesture for system, because of the sequence of continuous gestures. This being so, in order to interpret the meaning of individual gesture correctly, the interaction structure and framework are needed so that they can segment the indication of individual gesture. Method: We analyze 700 sign language words to structuralize the sign language gesture interaction. First of all, we analyze the transformational patterns of the hand gesture. Second, we analyze the movement of the transformational patterns of the hand gesture. Third, we analyze the type of other gestures except hands. Based on this, we design a framework for sign language interaction. Results: We elicited 8 patterns of hand gesture on the basis of the fact on whether the gesture has a change from starting point to ending point. And then, we analyzed the hand movement based on 3 elements: patterns of movement, direction, and whether hand movement is repeating or not. Moreover, we defined 11 movements of other gestures except hands and classified 8 types of interaction. The framework for sign language interaction, which was designed based on this mentioned above, applies to more than 700 individual gestures of the sign language, and can be classified as an individual gesture in spite of situation which has continuous gestures. Conclusion: This study has structuralized in 3 aspects defined to analyze the transformational patterns of the starting point and the ending point of hand shape, hand movement, and other gestures except hands for sign language interaction. Based on this, we designed the framework that can recognize the individual gestures and interpret the meaning more accurately, when meaningful individual gesture is input sequence of continuous gestures. Application: When we develop the system of sign language recognition, we can apply interaction framework to it. Structuralized gesture can be used for using database of sign language, inventing an automatic recognition system, and studying on the action gestures in other areas.

• Journal of the Institute of Convergence Signal Processing
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• v.14 no.4
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• pp.235-242
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• 2013

In this paper, the system which can translate sign language through motion recognition of Kinect camera system is developed for the communication between hearing-impaired person or language disability, and normal person. The proposed algorithm which can translate sign language is developed by using core function of Kinect, and two ways such as length normalization and elbow normalization are introduced to improve accuracy of translating sign langauge for various sign language users. After that the sign language data is compared by chart in order to know how effective these ways of normalization. The accuracy of this program is demonstrated by entering 10 databases and translating sign languages ranging from simple signs to complex signs. In addition, the reliability of translating sign language is improved by applying this program to people who have various body shapes and fixing measure errors in body shapes.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.8
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• pp.325-330
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• 2022

This paper conducts the collection and experiment of datasets for deep learning research on sign language such as sign language recognition, sign language translation, and sign language segmentation for Korean sign language. There exist difficulties for deep learning research of sign language. First, it is difficult to recognize sign languages since they contain multiple modalities including hand movements, hand directions, and facial expressions. Second, it is the absence of training data to conduct deep learning research. Currently, KETI dataset is the only known dataset for Korean sign language for deep learning. Sign language datasets for deep learning research are classified into two categories: Isolated sign language and Continuous sign language. Although several foreign sign language datasets have been collected over time. they are also insufficient for deep learning research of sign language. Therefore, we attempted to collect a large-scale Korean sign language dataset and evaluate it using a baseline model named TSPNet which has the performance of SOTA in the field of sign language translation. The collected dataset consists of a total of 11,402 image and text. Our experimental result with the baseline model using the dataset shows BLEU-4 score 3.63, which would be used as a basic performance of a baseline model for Korean sign language dataset. We hope that our experience of collecting Korean sign language dataset helps facilitate further research directions on Korean sign language.