Dictionary supported generation of English text from Pitman Shorthand scripted phonetic text
ABSTRACT The Pitman Shorthand Language (PSL) is a recording medium practised in all organizations, where English is the medium of transaction. It has the practical advantage of high speed of recording, more than 180 words per minute, because of which it is appreciably received. This recording medium continues to exist in spite of considerable developments in speech processing systems, which are not yet universally established. In order to exploit the vast transcribing potential of PSL a new area of research into automation of PSL processing is conceived. This paper describes the substitution of equivalent English words for the phonetic compositions of transcribed words, in the process of automatic generation of English text from a PSL document. Transcription is achieved by making use of two new types of dictionaries specifically developed and implemented for this purpose, one of them being a phonetic dictionary wherein the words are sequenced in phonetic order and the other being an extended conventional dictionary wherein the words are appended with additional details such as use domain, forms of verbs, etc. The proposed approach is tested with limited words in both dictionaries and is found to perform satisfactorily. However, the scope exists for addition of new words into these dictionaries.
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ABSTRACT: Handwritten shorthand systems were devised to enable writers to record information on paper at fast speeds, ideally at the speed of speech. While they have been in existence for many years it is only since the 17th Century that widespread usage appeared. Several shorthand systems flourished in the first half of the 20th century until the introduction and widespread use of electronic recording and dictation machines in the 1970's. Since then, shorthand usage has been in rapid decline, but has not yet become a lost skill. Pitman shorthand has been shown to possess unique advantages as a means of fast text entry which is particularly applicable to hand-held devices in mobile environments. This paper presents progress and critical research issues for a Pitman/Renqun Shorthand Online Recognition System. Recognition and transcription experiments are reported which indicate that a correct recognition and transcription rate of around 90% is currently possible.International Journal of Pattern Recognition and Artificial Intelligence 11/2011; 23(05). DOI:10.1142/S0218001409007405 · 0.56 Impact Factor
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ABSTRACT: An innovative solution to the lexical post processing of handwritten Pitman's shorthand, denoted as the Bayesian Network (BN) based transcription is discussed along with experimental results. Unlike the conventional phonetic based transliteration approach, the paper presents a novel primitive based transcription method along with the creation of a new machine readable Pitman's shorthand lexicon. The Bayesian Network representation is shown to be robust against stroke variation and highly effective for handling major ambiguities of handwritten Pitman's Shorthand, including unpredictable vowel omissions and unclear thicknesses between similar consonant strokes. Pitman's shorthand specific unigram-based rejection strategies are also introduced that are highly effective in finding the most likely candidate words for a given outline.TENCON 2012 - 2012 IEEE Region 10 Conference; 01/2012
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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.Eighth International Conference on Document Analysis and Recognition (ICDAR 2005), 29 August - 1 September 2005, Seoul, Korea; 01/2005