Lexicon Organisation and Contextual Methods for Online Handwritten Pitman's Shorthand Recognition

This research investigates innovations for the computer transcription of handwritten Pitman’s Shorthand as a rapid means of text entry (up to 100 words per minute) into today’s pen-based handheld devices. Two mathematical models are developed in this work. The first model deals with high level phonetic-based translation, while the second model is specifically concerned with low level primitive-based translation. Both models are closely related to the lexicon organization and contextual processing for online handwritten Pitman’s Shorthand recognition. A number of research issues that arise from interpreting handwritten Pitman’s Shorthand strokes of digital ink as text are addressed including: (a) a feasibility study into improving a conventional phonetic-based transliteration approach to advance word recognition; (b) an investigation into new Bayesian Network modelling of strokes and their relationships in order to solve the problem of geometric variations and vowel ambiguities of handwritten Pitman’s Shorthand; (c) generation of a new machine-readable Pitman’s Shorthand lexicon to facilitate the direct transcription of geometric features of Pitman’s Shorthand into English text; (d) analysis of the impact of statistical language modelling in handwriting phrase recognition; (e) and a discussion of the graphical user interface issues in relation to the development of a commercial prototype from the frame of reference of this research. The research has been carried out in close cooperation with Nanyang Technology University (NTU) in Singapore. The system is currently undergoing a final evaluation in terms of its recognition accuracy, as well as its potential to be introduced as a commercially viable fast text input system.