Saha, Sriparna and Lahiri, Rimita and Konar, Amit and Nagar, Atulya K. (2016) A Novel Approach To American Sign Language Recognition Using MAdaline Neural Network. In: IEEE SSCI 2016 Greece -, DECEMBER 6 TO 9, 2016, ATHENS, GREECE.
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Sign language interpretation is gaining a lot of research attention because of its social contributions which is proved to be extremely beneficial for the people suffering from hearing or speaking disabilities. This paper proposes a novel image processing sign language detection framework that employs MAdaline network for classification purpose. This paper mainly highlights two novel aspects, firstly it introduces an advanced feature set comprising of seven distinct features that has not been used widely for sign language interpretation purpose, more over utilization of such features negates the cumbersome step of cropping of irrelevant background image, thus reducing system complexity. Secondly it suggests a possible solution of the concerned problem can be obtained using an extension of the traditional Adaline network, formally termed as MAdaline Network. Although the concept of MAdaline network has originated much earlier, the provision of application of this framework in this domain definitely help in designing an improved sign language interpreting interface. The newly formulated framework has been implemented to recognize standardized American sign language containing 26 English alphabets from ‘A’ to ‘Z’. The performance of the proposed algorithm has also been compared with the standardized algorithms, and in each case the former one outperformed its contender algorithms by a large margin establishing the efficiency of the same.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Keywords: | American sign language; MAdaline; Adaline; Widrow-hoff’s learning rule; least mean square |
| Faculty / Department: | Faculty of Human and Digital Sciences > Mathematics and Computer Science |
| Depositing User: | Atulya Nagar |
| Date Deposited: | 01 Aug 2017 10:04 |
| Last Modified: | 01 Aug 2017 10:04 |
| URI: | https://hira.hope.ac.uk/id/eprint/1728 |
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