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Sign language is mainly employed by hearing-impaired people to communicate with each other. However, communication with normal people is a major handicap for them since normal people do not understand their sign language. Sign language recognition is needed for realizing a human oriented interactive system that can perform an interaction like norma...
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Hand gesture recognition serves as a key for overcoming many difficulties and providing convenience for human life. The ability of machines to understand human activities and their meaning can be utilized in a vast array of applications. One specific field of interest is sign language recognition. This paper provides a thorough review of state-of-t...
Hand gesture is one of the most prominent ways of communication since the beginning of the human era. Hand gesture recognition extends human-computer interaction (HCI) more convenient and flexible. Therefore, it is important to identify each character correctly for calm and error-free HCI. Literature survey reveals that most of the existing hand ge...
American Sign Language Recognition (ASLR) system is a process where the computer understands the gestures of American Sign Language (ASL) automatically and converts them into their equivalent human / machine readable text. Several researchers have presented various vision based techniques to recognize static and dynamic gesture recognition of ASL....
Among many of the fastest growing research fields, sign language recognition is one of the top. Deaf and dumb community uses sign language to express their ideas or views. Sign Language is a methodical coded language where meanings are assigned to every gestures. Many techniques have been developed with the advancement of science and technology to...
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... Gambar 2.1 Contoh Bahasa Isyarat[11] B. Pemrosesan Teks Pemrosesan teks atau text processing mengacu pada makanisme penciptaan atau manipulasi teks elektronis[31]. Teks biasanya mengacu pada semua kata alfanumerik sesuai dengan papan tombol yang digunakan pengguna. ...
ABSTRAK Intisari – Menurut data Survei Sosial Ekonomi Nasional (Susenas) pada tahun 2012 terdapat sekitar 9,9 juta anak Indonesia menyandang disabilitas. Sekitar 7.87% dari total jumlah penyandang disabilitas tersebut mengalami tunarungu atau keterbatasan mendengar. Penyandang tunarungu melakukan komunikasi dengan menggunakan Bahasa isyarat. Karena tidak semua orang mengerti dengan bahasa isyarat maka dibutuhkan alat bantu atau aplikasi untuk berkomunikasi dengan penyandang tunarungu. Keterbatasan dalam berkomunikasi antara orang biasa dengan penyandang tunarungu. Oleh karena ity, untuk membantu mahasiswa dan dosen berkomunikasi dengan mahasiswa yang tunarung maka dibutuhkan aplikasi kamus Bahasa isyarat dengan Speech Recognition. Pengembangan aplikasi ini menggunakan metode pengembangan aplikasi waterfall. Dimana setiap alur berjalan secara selaras dan memudahkan untuk mencari kesalahan system. Pengujian dilakukan dengan verifikasi kebutuhan untuk memastikan produk perangkat lunak yang dihasilkan sesuai dengan spesifikasi yang ditentukan. Kata Kunci: Bahasa isyarat; kamus; speech recognition; ABSTRACT Digest - According to data from the National Socio-Economic Survey (Susenas) in 2012 there were around 9.9 million Indonesian children with disabilities. Around 7.87% of the total number of persons with disabilities experience hearing impairment or hearing impairment. People with hearing impairment communicate using sign language. Because not everyone understands sign language, tools or applications are needed to communicate with deaf people. Limitations in communicating between ordinary people and hearing impaired people. Therefore, to help students and lecturers communicate with students who are fussy, it requires a sign language dictionary application with Speech Recognition. This application development uses the waterfall application development method. Where each flow runs in harmony and makes it easy to find system errors. The test is carried out by verifying the need to ensure that the software product is produced according to the specified specifications. Keywords: Signal language; dictionary; speech recognition;
... Video is captured in the form of video files before being processed by using image processing [4]. The hand gestures recognition has recently become an important field of research focusing on hand gestures recognition [5]. ...
In this research the proposes a method of recognition of BISINDO letters based on hand-shape features that hint every shape of BISINDO Letters. In outline, this method is divided into two parts: the first is part of formation database shape features of BISINDO letters A-Z and the second is part of BISINDO letters recognition. In the first section consist of hand-shape image acquisition that hint every BISINDO letters, segmentation process, edge detection process, feature extraction process that is probability value of hand-shape chain code occurrence and process of database feature formation. In the second section is consist of hand-shape image acquisition process as BISINDO letters query followed by segmentation process, edge detection process, hand-shape feature extraction and recognition process by using calculation difference in distance between query shape feature to each shape feature in database feature. The image acquisition process in two parts above conducted directly (real time) via Webcam connected to the computer device. The method above has been implemented into prototype of Bisindo letters recognition software interface. The experiment results show the accuracy level of BISINDO letter recognition (26 BISINDO letters A to Z) which is reaching above 95%.
... Video is captured in the form of video files before being processed by using image processing [4]. The hand gestures recognition has recently become an important field of research focusing on hand gestures recognition [5]. ...
In this research the proposes a method of recognition of BISINDO letters based on hand-shape features that hint every shape of BISINDO Letters. In outline, this method is divided into two parts: the first is part of formation database shape features of BISINDO letters A-Z and the second is part of BISINDO letters recognition. In the first section consist of hand-shape image acquisition that hint every BISINDO letters, segmentation process, edge detection process, feature extraction process that is probability value of hand-shape chain code occurrence and process of database feature formation. In the second section is consist of hand-shape image acquisition process as BISINDO letters query followed by segmentation process, edge detection process, hand-shape feature extraction and recognition process by using calculation difference in distance between query shape feature to each shape feature in database feature. The image acquisition process in two parts above conducted directly (real time) via Webcam connected to the computer device. The method above has been implemented into prototype of Bisindo letters recognition software interface. The experiment results show the accuracy level of BISINDO letter recognition (26 BISINDO letters A to Z) which is reaching above 95%.
... Video is captured in the form of video files before being processed by using image processing [4]. The hand gestures recognition has recently become an important field of research focusing on hand gestures recognition [5]. ...
In this research the proposes a method of recognition of BISINDO letters based on hand-shape features that hint every shape of BISINDO Letters. In outline, this method is divided into two parts: the first is part of formation database shape features of BISINDO letters A-Z and the second is part of BISINDO letters recognition. In the first section consist of hand-shape image acquisition that hint every BISINDO letters, segmentation process, edge detection process, feature extraction process that is probability value of hand-shape chain code occurrence and process of database feature formation. In the second section is consist of hand-shape image acquisition process as BISINDO letters query followed by segmentation process, edge detection process, hand-shape feature extraction and recognition process by using calculation difference in distance between query shape feature to each shape feature in database feature. The image acquisition process in two parts above conducted directly (real time) via Webcam connected to the computer device. The method above has been implemented into prototype of Bisindo letters recognition software interface. The experiment results show the accuracy level of BISINDO letter recognition (26 BISINDO letters A to Z) which is reaching above 95%.
... Dynamic Time Warping (DTW) is a well-known technique for finding optimal alignment between two time-dependent sequences under a certain limit [23]. DTW is also called as a non-linear sequence alignment [24]. DTW is more realistic to use in measuring pattern matching between two time-dependent sequences than just using linear measurement algorithms such as Euclidean Distance, Manhattan, Canberra, and Mexican Hat; and widely used for speech recognition, handwriting recognition and signatures, data mining, clustering, gesture processing, and music. ...
Sign Language Recognition System (SLRS) is a system to recognise sign language and then translate them into text. This system can be developed by using a sensor-based technique. Some studies have implemented various feature extraction and classification methods to recognise sign language in the different country. However, their systems were user dependent (the accuracy was high when the trained and the tested user were the same people, but it was getting worse when the tested user was different to the trained user). Therefore, in this study, we proposed a feature extraction method which is invariant to a user. We used the distance between two users’ skeleton instead of using the users’ skeleton positions because the skeleton distance is independent to the user posture. Finally, forty-five features were extracted in this proposed method. Further, we classified the features by using a classification method that is suitable with sign language gestures characteristic (time-dependent sequence data). The classification method is Dynamic Time Wrapping. For the experiment, we used twenty Indonesian sign languages from different semantic groups (greetings, questions, pronouns, places, family and others) and different gesture characteristic (static gesture and dynamic gesture). Then the system was tested by a different user with the user who did the training. The result was promising, this proposed method produced high accuracy, reach 91% which shows that this proposed method is user independent.
... Dengan begitu, pemilihan metode penangkapan gambar serta metode klasifikasinya sangat berpengaruh. Karena itu, sebagai solusinya digunakan hybrid fuzzy dan Neural Network dengan isyarat yang ditangkap berbasis kamera Kinect [8]. ...
Deafness is a condition where an individual's hearing cannot function normally. So, sign language was created which was used as a solution to the problem. In Indonesia, the sign languages that are known are SIBI (Indonesian Sign Language System) and BISINDO (Indonesian Sign Language). Although SIBI has been recognized by the Indonesian government, in its use it is less desirable. This research was conducted to identify empty hand signals. Where it will help the user naturally without additional assistance. Experiments carried out using a dataset that was demonstrated by 1 display. In the process, the characteristics of the hand are taken using the Histogram Oriented Gradient (HOG) method. Whereas to separate it from the background image, color segmentation is used. The results of the process are then taken to classify. The classification process uses the Adaptive Neuro-Fuzzy Inference System method. The results of the tests carried out resulted in an accuracy of 78.31%. The problem is done.
... Several studies have been reported that review prior works in order to suggest the best method in Sign Language Recognition system. Majid and Zain (2013) reviewed the development of Sign Language Recognition system for different sign languages. They reviewed only 32 related publications up to year 2012. ...
Sign Language is the only method used in communication between the hearing-impaired community and common community. Sign Language Recognition (SLR) system, which is required to recognize sign languages, has been widely studied for years. The studies are based on various input sensors, gesture segmentation, extraction of features and classification methods. This paper aims to analyze and compare the methods employed in the SLR systems, classifications methods that have been used, and suggests the most promising method for future research. Due to recent advancement in classification methods, many of the recent proposed works mainly contribute on the classification methods, such as hybrid method and Deep Learning. This paper focuses on the classification methods used in prior Sign Language Recognition system. Based on our review, HMM-based approaches have been explored extensively in prior research, including its modifications. Deep Learning such as Convolutional Neural Network is popular in the past five years. Hybrid CNN-HMM and fully Deep Learning approaches have shown promising results and offer opportunities for further exploration. However, overfitting and high computational requirements still hinder their adoption. We believe the future direction of the research is toward developing a simpler network that can achieve high performance and requires low computational load, which embeds the feature learner into the classifier in multi-layered neural network fashion.
... Table 2 summarizes some proposed works on glove-based hand tracking methods and their reported accuracies. A review conducted in [16] mentioned that glove-based approach has a disadvantage. The gloves need to be worn, and the idea of using gloves will hamper the naturalness of human computer interaction. ...
... Another advantage of using Microsoft Kinect is the independency of lighting condition. Camera will still detect equally well even in the places with less light [16]. ...
... They do two experiments with unsatisfactory result. The main obstacle in this study the number of errors during the conversion of text to video / avatar of sign language barriers particularly in terms of programming [6]. ...
... The presentation of all works related to SLR is not the intention of this section and more can be found on [27], [36] and [39]. The following section present works that employed Kinect device for building SLR systems. ...
Sign language is used for communication among hearing impaired people. Their communication is hardly understood by normal hearing people. To facilitate communication between these two groups this paper presents a system that uses Microsoft Kinect device to build a translation system that translates signs from sign language to spoken language. The developed system uses SigmaNIL framework to provide hand shape recognition which is not supported by official and other SDKs for Kinect. The resulting system is capable of translating a limited vocabulary of Kosova Sign Language. The system has been tested with native and non-native speakers of sign language and in general achieves high accuracy varying by components of sign language.
