Evaluation of Feature Sets in the Post Processing of Handwritten Pitman's Shorthand

Abstract

Innovative ways to rapidly input text becomes essential in today's world of mobile computing. The paper discusses the computer transcription of handwritten Pitman shorthand as a means of rapid text entry to pen-based computers, particularly from the aspect of linguistic post processing. Feature-to-phoneme conversion is introduced as the first stage of a text interpreter and the application of various production rules based on different pattern structures is discussed. It demonstrates that phoneme ordering is compulsory in dictionary-based transcription and the use of an approximate pattern-matching algorithm resolves the problem of recognition confusion between similar patterns. Experimental results are promising and demonstrate an overall accuracy of 84%.