Malaysian Sign Language (MSL) is the main language that is commonly used by the hearing and speech impaired person in Malaysian. The SL (SL) involves hand movement, and hand gestures. In order to help people who are not familiar, but need to understand a particular SL, an automatic SL recognition system is highly required. The research in this area, especially for MSL, has been conducted by many researchers, but one of the main challenges in this research is the availability of suitable sign database for the recognition. The existing databases, especially which of MSL database, are provided often without a proper standard of image resolution, structure and compression that are sufficiently good for research purpose. To provide comprehensive information for the research on MSL, the MSL database is highly required. In this project, a MSL database is developed. The database is the first of the kind and developed for research purpose. In general, the structure of the MSL database is classified into groups that deal with the hand movement, hand gestures, and hand location. For the classification in our proposed database, the MSL is classified into One Hand, Two Hands, Static, and Dynamic. This classification is made to ease researchers in defining the research method for each type offhand signing.

Natural Language Processing (NLP) is a method which works on any language processing. Some of the algorithms are based on edit distance analysis. It is a process where the statistical calculations between two words or sentences are analyzed. Some of used edit distances for NLP are Levenshtein, Jaro Wrinkler, Soundex, N-grams, and Mahalanobis.

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Language Processing Unit (LPU) is a system built to process text-based data to comply with the rules of sign language grammar. This system was developed as an important part of the sign language synthesizer system. Sign language (SL) uses different grammatical rules from the spoken/verbal language, which only involves the important words that Hearing/Impaired Speech people can understand. Therefore, it needs word classification by LPU to determine grammatically processed sentences for the sign language synthesizer. However, the existing language processing unit in SL synthesizers suffers time lagging and complexity problems, resulting in high processing time. The two features, i.e., the computational time and success rate, become trade-offs which means the processing time becomes longer to achieve a higher success rate. This paper proposes an adaptive Language Processing Unit (LPU) that allows processing the words from spoken words to Malaysian SL grammatical rule that results in relatively fast processing time and a good success rate. It involves n-grams, NLP, and Hidden Markov Models (HMM)/Bayesian Networks as the classifier to process the text-based input. As a result, the proposed LPU system has successfully provided an efficient (fast) processing time and a good success rate compared to LPU with other edit distances (Mahalanobis, Levensthein, and Soundex). The system has been tested on 130 text-input sentences with several words ranging from 3 to 10 words. Results showed that the proposed LPU could achieve around 1.497ms processing time with an average success rate of 84.23% for a maximum of ten-word sentences.

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