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Title: Transliteration of Online Handwritten Phonetic Pitman's Shorthand with the Use of a Bayesian Network
Contributor(s): Htwe, Swe Myo (author); Higgins, Colin (author); Leedham, Graham  (author); Yang, Ma (author)
Publication Date: 2005
DOI: 10.1109/ICDAR.2005.244
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Abstract: This paper presents a detailed view of a novel solution to the computer transcription of handwritten Pitman's shorthand as a means of rapid text entry (up to 100 words per minute) into today's handheld devices with the use of a Bayesian network representation. Detailed design considerations of Bayesian network based shorthand outline models, including hypothesis of missing vowel components occurring in speed writing and unclear thickness and length of electrical pen-strokes are presented, along with graphical examples. Although Pitman's shorthand is written phonetically, our outline models are also based on low-level geometric attributes rather than phonetic attributes with the intention of coping with the unique features of handwritten Pitman's shorthand. The experimental results indicate an average accuracy of 92.86%, which is a marked improvement over previous applications of the same framework.
Publication Type: Conference Publication
Conference Details: ICDAR 2005: 8th International Conference on Document Analysis and Recognition, Seoul, Korea, 29th August - 1st September, 2005
Source of Publication: Proceedings of the 2005 Eight International Conference on Document Analysis and Recognition (ICDAR'05), v.2, p. 1090-1094
Publisher: IEEE: Institute of Electrical and Electronics Engineers
Place of Publication: United States of America
ISSN: 1520-5263
Field of Research (FOR): 080109 Pattern Recognition and Data Mining
080104 Computer Vision
080106 Image Processing
Socio-Economic Objective (SEO): 890299 Computer Software and Services not elsewhere classified
810107 National Security
Peer Reviewed: Yes
HERDC Category Description: E1 Refereed Scholarly Conference Publication
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