WALAILAK JOURNAL OF SCIENCE AND TECHNOLOGY Volume 18, No. 13, Month JULY, Year 2021, Pages -
Automatic thai finger spelling transcription
Pisit NAKJAI, Tatpong KATANYUKUL
Abstract Download PDFThis article explores a transcription of a video recording Thai Finger Spelling (TFS)—a specific signing mode used in Thai sign language—to a corresponding Thai word. TFS copes with 42 Thai alphabets and 20 vowels using multiple and complex schemes. This leads to many technical challenges uncommon in spelling schemes of other sign languages. Our proposed system, Automatic Thai Finger Spelling Transcription (ATFS), processes a signing video in 3 stages: ALS marking video frames to easily remove any non-signing frame as well as conveniently group frames associating to the same alphabet, SR classifying a signing image frame to a sign label (or its equivalence), and SSR transcribing a series of signs into alphabets. ALS utilizes the TFS practice of signing different alphabets at different locations. SR and SSC employ well-adopted spatial and sequential models. Our ATFS has been found to achieve Alphabet Error Rate (AER) 0.256 (c.f. 0.63 of the baseline method). In addition to ATFS, our findings have disclosed a benefit of coupling image classification and sequence modeling stages by using a feature or penultimate vector for label representation rather than a definitive label or one-hot coding. Our results also assert the necessity of a smoothening mechanism in ALS and reveal a benefit of our proposed WFS, which could lead to over 15.88 % improvement. For TFS transcription, our work emphasizes the utilization of signing location in the identification of different alphabets. This is contrary to a common belief of exploiting signing time duration, which are shown to be ineffective by our data.
Sign Language Transcription, Sign Sequence Classification, Signing Video Transcription, Thai Finger Spelling Transcription