Method and Apparatus for Symbol Recognition Using Multidimensional Preprocessing and Symbol Sorting

Data samples describing a plurality of micro-segments that compose a symbol to be recognized are received from a device (160) such as an electronic pad. A preprocessor (162) maps the micro-segments into cells of an array that has several feature dimensions. The preprocessor assigns values to the cells based on the length of a micro-segment associated with the cell, and how well the features of the associated micro-segment correspond to the feature label of the cell. The cell values are used as inputs to at least one of a plurality of neural networks, where each neural network (168) is trained to identify symbols from a different group of symbols. A sorter (164) examines the symbol to determine which neural network should be used to recognize the symbol. An output produced by the sorter controls a switching means (166) that communicates the cell values to the proper neural network.