| 摘要 |
A bounds evaluation unit generates a bounds measurement pair corresponding to a character pattern pair determined from a handwritten word. Within the bounds measurement pair, a first bounds measurement corresponds to a first character pattern, and a second bounds measurement corresponds to a second character pattern. The first and second bounds measurements are each a bounding box that defines a left-most, a right-most, a top-most, and a bottom-most extent of the corresponding character pattern. The bounds measurement pair is compared against one or more bounds model pairs, where each bounds model pair corresponds to a hypothesized character identifier pair in which each individual hypothesized character identifier has been determined by a character recognition unit. Each bounds model pair indicates the expected size and position of a character pattern pair corresponding to the hypothesized character identifier pair. Bounds model pairs are trained based upon size and positional relationships between character pattern pairs within handwritten training words. The comparison of the bounds measurement pair against a given bounds model pair produces a set of error values. The error values are used in conjunction with an error covariance matrix that has also been trained with handwritten training words to determine a pairwise cost value associated with the hypothesized character identifier pair. A word recognition unit incorporates individual character cost values and each pairwise cost value in a conventional minimum-cost path search method to determine the identity of the handwritten word. |
