A Handwriting Recognition System based on the Properties and Architectures of the Human Motor System
Lambert Schomaker, Hans-Leo H. M. Teulings · 1990
The human reader of handwriting is unaware of the amount of back-ground knowledge that is constantly being used by a massive parallel computer, his brain, to decipher cursive script. Artificial cursive script recognizers do not have access to a comparable source of knowledge or of comparable computational power to perform top-down processing. Therefore, in an artificial script recognizer, there is a strong demand for reliable bottom-up processing. For the recognition of unrestricted script consisting of arbitrary character sequences, on-line recorded handwriting signals offer a more solid basis than the optically obtained grey-scale image of a written pen trace, because of the temporal information and the inherent vectorial description of shape. The enhanced bottom-up processing is based on implementing knowledge of the motor system in the handwriting recognition system. The bottom-up information will already be sufficient to recognize clearly written and unambiguous input. However, ambiguous shape sequences, such as m vs n.. or d vs cl, and sloppy stroke patterns still require top-down processing. The present paper discusses the handwriting recognition system as being developed at the NICI. The system contains six major modules: (1) On-line digitizing, pre-processing of the movements and segmentation into strokes. (2) Normalization of global handwriting parameters. (3) Extraction of motorically invariant, real-valued, feature values per stroke to form a multidimensional