IEEE International Conference on Advancement in Electrical and Electronic Engineering - (ICAEEE 2018), 2018
Sign Language is the communication standard for people who have hearing and speaking deficiency, ... more Sign Language is the communication standard for people who have hearing and speaking deficiency, usually called deaf and dumb. It is the only way for such people to communicate. This paper proposes a model which would help in recognizing the different signs in American Sign Language. As this is still an emerging field of research, the dataset available in this topic is very noisy. In this paper, we proposed some image processing based operations such as Logarithmic Transformation, Histogram Equalization etc. to reduce the noise from the images of the dataset. Then, canny edges are detected from the segmented image of signs. After that, the proposed method identifies the signs based on the features extracted using Histogram of Oriented Gradients (HOG) Feature Extraction strategy. The extracted features of the signs are classified using KNN classifier. The experimental result shows that the proposed method offers better classification accuracy (94.23%) in comparison to the method base...
Journal of Theoretical and Applied Information Technology, 2020
Deaf and dumb people usually use sign language as a means of communication. This language is made... more Deaf and dumb people usually use sign language as a means of communication. This language is made up of manual and non-manual physical expressions that help the people to communicate within themselves and with the normal people. Sign language recognition deals with recognizing these numerous expressions. In this paper, a model has been proposed that recognizes different characters of Bengali sign language. Since the dataset for this work is not readily available, we have taken the initiative to make the dataset for this purpose. In the dataset, some pre-processing techniques such as Histogram Equalization, Lightness Smoothing etc. have been performed to enhance the signs' image. Then, the skin portion from the image is segmented using YCbCr color space from which the desired hand portion is cut out. After that, converting the image into grayscale the proposed model computes the Histogram of Oriented Gradients (HOG) features for different signs. The extracted features of the signs' are used to train the K-Nearest Neighbors (KNN) classifier model which is used to classify various signs. The experimental result shows that the proposed model produces 91.1% accuracy, which is quite satisfactory for real-life setup, in comparison to other investigated approaches.
Unlike English characters, one of the major drawbacks in recognizing handwritten Bengali script i... more Unlike English characters, one of the major drawbacks in recognizing handwritten Bengali script is the massive amount of characters in Bengali language and their complex shapes. There are 50 complex shaped characters in Bengali alphabet set and working with this huge amount of characters with an appropriate set of feature is a tough problem to solve. Moreover, the ambiguity and precision error are common in handwritten words. In addition, among the huge amount of complex shaped characters, some are very similar in shape those possess severe difficulty to recognize handwritten Bengali characters. Bearing in mind the complexity of the problem, an efficient approach for recognizing handwritten Bengali alphabet is proposed in this work. This proposed approach for identifying Bengali characters is based on character geometry-oriented feature extraction for different handwritten characters. In this paper, different image processing steps are used including image acquisition, digitization ...
IEEE International Conference on Advancement in Electrical and Electronic Engineering - (ICAEEE 2018), 2018
Sign Language is the communication standard for people who have hearing and speaking deficiency, ... more Sign Language is the communication standard for people who have hearing and speaking deficiency, usually called deaf and dumb. It is the only way for such people to communicate. This paper proposes a model which would help in recognizing the different signs in American Sign Language. As this is still an emerging field of research, the dataset available in this topic is very noisy. In this paper, we proposed some image processing based operations such as Logarithmic Transformation, Histogram Equalization etc. to reduce the noise from the images of the dataset. Then, canny edges are detected from the segmented image of signs. After that, the proposed method identifies the signs based on the features extracted using Histogram of Oriented Gradients (HOG) Feature Extraction strategy. The extracted features of the signs are classified using KNN classifier. The experimental result shows that the proposed method offers better classification accuracy (94.23%) in comparison to the method base...
Journal of Theoretical and Applied Information Technology, 2020
Deaf and dumb people usually use sign language as a means of communication. This language is made... more Deaf and dumb people usually use sign language as a means of communication. This language is made up of manual and non-manual physical expressions that help the people to communicate within themselves and with the normal people. Sign language recognition deals with recognizing these numerous expressions. In this paper, a model has been proposed that recognizes different characters of Bengali sign language. Since the dataset for this work is not readily available, we have taken the initiative to make the dataset for this purpose. In the dataset, some pre-processing techniques such as Histogram Equalization, Lightness Smoothing etc. have been performed to enhance the signs' image. Then, the skin portion from the image is segmented using YCbCr color space from which the desired hand portion is cut out. After that, converting the image into grayscale the proposed model computes the Histogram of Oriented Gradients (HOG) features for different signs. The extracted features of the signs' are used to train the K-Nearest Neighbors (KNN) classifier model which is used to classify various signs. The experimental result shows that the proposed model produces 91.1% accuracy, which is quite satisfactory for real-life setup, in comparison to other investigated approaches.
Unlike English characters, one of the major drawbacks in recognizing handwritten Bengali script i... more Unlike English characters, one of the major drawbacks in recognizing handwritten Bengali script is the massive amount of characters in Bengali language and their complex shapes. There are 50 complex shaped characters in Bengali alphabet set and working with this huge amount of characters with an appropriate set of feature is a tough problem to solve. Moreover, the ambiguity and precision error are common in handwritten words. In addition, among the huge amount of complex shaped characters, some are very similar in shape those possess severe difficulty to recognize handwritten Bengali characters. Bearing in mind the complexity of the problem, an efficient approach for recognizing handwritten Bengali alphabet is proposed in this work. This proposed approach for identifying Bengali characters is based on character geometry-oriented feature extraction for different handwritten characters. In this paper, different image processing steps are used including image acquisition, digitization ...
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Papers by Iqbal Mahmud