4. In an automatic character recognition system according to claim 3, a scanning and training structure wherein said manifesting and processing means processes and manifests a plurality of different known characters from said character class in sequence and wherein said operation functions of said scanning and training structure are repeated for each of said different known characters of said plurality to produce storage indicia in each of said k storage planes at discrete storage locations corresponding to the columns associated with of said plurality of different known characters and to the rows associated with said recorded shape indicies and angle values produced relative to each of said separate k scans using different sets of selected scan parameters for said plurality of different known characters.
4. In an automatic character recognition system according to claim 3, a scanning and training structure wherein said manifesting and processing means processes and manifests a plurality of different known characters from said character class in sequence and wherein said operation functions of said scanning and training structure are repeated for each of said different known characters of said plurality to produce storage indicia in each of said k storage planes at discrete storage locations corresponding to the columns associated with of said plurality of different known characters and to the rows associated with said recorded shape indicies and angle values produced relative to each of said separate k scans using different sets of selected scan parameters for said plurality of different known characters. 6. In an automatic character recognition system according to claim 4, means for identifying an unknown character of said class of a plurality of known characters after said scanning and training operation, including: means for entering said unknown character into said processing and manifesting means for manifesting said unknown character in a digital matrix format of stored battery indicia, means for repeatedly digitally scanning said stored unknown character indicia k separate times using a k different sets of selected scan parameters one for each separate scan, each of said selected scan parameters designating a specific start position, scan length, binary indicia transition and direction for said scanning function, said digital scanning function producing a k binary encoded path vectors in the form of k multi-bit binary words including the starting coordinates of each of said scans and a plurality of further binary coordinates of said unknown character along said selected lengths of each of said scans, wherein said complex inner product function performing means performs the complex inner product function of each of said k binary encoded path vectors produced by scanning said unknown character and each of said stored canonic shapes to produce k separate pluralities of complex numbers encoded in binary form, each one of said k separate pluralities of encoded complex numbers including an encoded complex number for each complex inner product performed with each canonic shape and a separate different one of said k binary encoded path vectors, each of said binary encoded complex inner product numbers including a magnitude value representative of the similarity between its associated one of said k binary encoded path vectors and each of said canonic shapes, and an angle value representative of the angular offset of its associated one of said k binary encoded path vectors relative to each of said canonic shapes, wherein said magnitude determining and recording means and said shape index and angle value encoding means provide a plurality of k separate N bit vectors, one for each of said k different scans of said unknown character, said k separate N bit vectors being representative of the shape index and angle value of the largest magnitude inner complex number produced by each of said k scan path vectors and said plurality of canonic shapes, and wherein said first of said k separate N bit vectors specifies a specific row of the first one of said k storage planes, said second of said N bit vectors specifies a specific row of the second one of said k storage planes, and so on to said kth N bit vector which specifies a specific row of the kth one of said storage planes, means for obtaining and storing the contents of each of said specified k rows, one from each of said k storage planes, specified by each of said k-N bit vectors, means for performing the product vector of the value of the first column location contents of each of said specified k rows, and for the values of each of the succeeding column locations of each of the other specified k rows to produce a plurality of products vector components, one for each column location of said rows, means for determining and selecting the largest one of said components of said product vector and storing the column location of said largest product vector component, and means for indicating the known character associated with said one selected column location associated with said largest product vector component, said unknown character being recognized as said known character.