Abstract: A portable bar code reader including a keyboard and a bar code system computer wherein the bar code system computer is constructed for executing a standard operating system to enable the bar code system computer to execute standard application programs written for the standard operating system. The keyboard includes a keypad coupled to a keyboard processor and a keyboard memory wherein the keyboard memory is constructed to store a plurality of token data units. The keypad includes a plurality of keys and is constructed to provide a plurality of X-Y position signals wherein each X-Y position signal is indicative of an associated key being depressed. The keypad also includes a change meaning assembly constructed to provide a change meaning signal to the keyboard processor 308. The keyboard processor 308 is responsive to the change meaning signal to select a portion of the keyboard memory that stores the meanings of the keys associated with the change meaning signal.

Abstract: A character recognition method comprising the following steps: (1) acquiring a two dimensional array of pixels, (2) locating an unknown character in the two dimensional array, (3) computing weighted correlation coefficients between the unknown character and a trained set of characters (i.e. a font), (4) recognizing the unknown character as the trained character with the highest weighted correlation coefficient above a threshold. The weights in the correlation calculations are adjusted to place more emphasis on those areas of a trained character that distinguishes it from all other trained characters in the training set. A method for optimally adjusting these weights is described herein.

Abstract: A character recognition method comprising the following steps: (1) acquiring a two dimensional array of pixels, (2) locating an unknown character in the two dimensional array, (3) computing statistically weighted correlation coefficients between the unknown character and a trained set of characters (i.e. a font), (4) recognizing the unknown character as the trained character with the highest correlation coefficient above a threshold. The weights in the correlation calculations are adjusted to place more emphasis on those areas of a character that exhibit less noise and less emphasis on those areas of the character that exhibit more noise. A method for determining and using these weights is described herein.

Abstract: A character recognition method comprising the following steps: (1) acquiring a two dimensional array of pixels, (2) locating an unknown character in the two dimensional array, (3) computing correlation coefficients between an unknown character and a trained set of characters (i.e., a font), (4) recognizing the unknown character as the trained character with the highest correlation coefficient above a threshold. The manner in which trained characters are chosen is described herein as a correlation search. Correlation search results in a fast means of correctly classifying an unknown character from a large training set, often on the second or third try, depending on the inner-correlations of the trained characters.