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Sign language is a main communication channel among hearing disabilitycommunity. Automatic sign language transcription could facilitate bettercommunication and understanding between hearing disability community andhearing majority. As a recent work in automatic sign language transcription hasdiscussed, effectively handling or identifying a non-sign posture is one of thekey issues. A non-sign posture is a posture unintended for sign reading anddoes not belong to any valid sign. A non-sign posture may arise during signtransition or simply from an unaware posture. Confidence ratio has beenproposed to mitigate the issue. Confidence ratio is simple to compute andreadily available without extra training. However, confidence ratio is reportedto only partially address the problem. In addition, confidence ratioformulation is susceptible to computational instability. This article proposesalternative formulations to confidence ratio, investigates an issue of non-signidentification for Thai Finger Spelling recognition, explores potentialsolutions and has found a promising direction. Not only does this findingaddress the issue of non-sign identification, it also provide some insightbehind a well-learned inference machine, revealing hidden meaning and newinterpretation of the underlying mechanism. Our proposed methods are evaluatedand shown to be effective for non-sign detection.

Automatic Hand Sign Recognition: Identify Unusuality through Latent Cognizance