On-line recognition of connected handwriting
Computer technology has rapidly improved over the last few years, with more powerful machines becoming ever smaller and cheaper. The latest growth area is in portable personal computers, providing powerful facilities to the mobile business person. Alongside this development has been the vast improvement to the human computer interface, allowing noncomputer- literate users access to computing facilities. These two aspects are now being combined into a portable computer that can be operated with a stylus, without the need for a keyboard. Handwriting is the obvious method for entering data and cursive script recognition research aims to comprehend unconstrained, natural handwriting. The ORCHiD system described in this thesis recognises connected handwriting collected on-line, in real time, via a digitising pad. After preprocessing, to remove any hardware-related errors, and normalising, the script is segmented and features of each segment measured. A new segmentation method has been developed which appears to be very consistent across a large number of handwriting styles. A statistical template matching algorithm is used to identify the segments. The system allows ambiguous matching, since cursive script is an ambiguous communications medium when taken out of context, and a probability for each match is calculated. These probabilities can be combined across the word to produce a ranked list of possible interpretations of the script word. A fast dictionary lookup routine has been developed enabling the sometimes very large list of possible words to be verified. The ORCHiD system can be trained, if desired, to a particular user. The training routine, however, is automatic since the untrained recognition system is used as the basis for the trained system. There is therefore very little start-up time before the system can be used. A decision-directed training approach is used. Recognition rates for the system vary depending on the consistency of the writing. On average, the untrained system achieved 75% recognition. After some training, average recognition rates of 91% were achieved, with up to 96% observed after further training.