From Off-line to On-line Handwriting Recognition

On-line handwriting includes more information on time order of the writing signal and on the dynamics of the writing process than off-line handwriting. Therefore, on-line recognition systems achieve higher recognition rates. This can be concluded from results reported in the literature, and has been demonstrated empirically as part of this work. We propose a new approach for recovering the time order of the off-line handwriting signal. Starting from an over-segmentation of the off-line handwriting into regular and singular parts, the time ordering of these parts and recognition of the word are performed simultaneously. This approach, termed ``OrdRec\\\'\\\', is based on a graph description of the handwriting signal and a recognition process using Hidden Markov Models (HMM). A complete omni-scriptor isolated word recognition system has been developed. Using a dynamic lexicon and models for upper and lower case characters, our system can process binary and gray value word images of any writing style (script, cursive or mixed). Using a dual handwriting data base which features both the on-line and the off-line signal for each of the 30000 words written by about 700 scriptors, we have shown experimentally that such an off-line recognition system, using the recovered time order information, can achieve recognition performances close to those of an on-line recognition system