Gale Annita Martin

US Patent:

55009050, Mar 19, 1996

Filed:

Mar 16, 1992

Appl. No.:

7/851841

Inventors:

Gale L. Martin - Austin TX
James A. Pittman - Austin TX
Mosfeq Rashid - Austin TX

Assignee:

Microelectronics and Computer Technology Corporation - Austin TX

International Classification:

G06K 962

US Classification:

382157

Abstract:

A multi-layered pattern recognition neural network (30) is disclosed that comprises an input layer (50) that is operable to be mapped onto an input space that includes a scan window (32). Two hidden layers (54) and (58) map the input space to an output layer (34). The hidden layers utilize a local receptor field architecture and store representations of objects within the scan window (32) for mapping into one of a plurality of output nodes. Further, the output layer (34) is also operable to store representations of desired distances between the center of the scan window (32) and the next adjacent object thereto and also the distance between the center of the scan window (32) and the center of the current object. A scanning system can then utilize the information regarding the distance to the next adjacent object, which is stored in an output vector (40) to incrementally jump to the center of the next adjacent character rather than scan the entire distance therebetween. This is referred to as a saccade operation.

US Patent:

54406510, Aug 8, 1995

Filed:

Apr 20, 1993

Appl. No.:

8/049967

Inventors:

Gale L. Martin - Austin TX

Assignee:

Microelectronics and Computer Technology Corp. - Austin TX

International Classification:

G06K 962

US Classification:

382156

Abstract:

A multi-layered pattern recognition neural network that comprises an input layer (28) that is operable to be mapped onto an input space comprising a scan window (12). Two hidden layers (30) and (32) map the input space to an output layer (16). The hidden layers utilize a local receptor field architecture and store representations of objects within the scan window (12) for mapping into one of a plurality of output nodes. Each of the plurality of output nodes and associated representations stored in the hidden layer define an object that is centered within the scan window (12). When centered, the object and its associated representation in the hidden layer result in activation of the associated output node. The output node is only activated when the character is centered in the scan window (12). As the scan window (12) scans a string of text, the output nodes are only activated when the associated character moves within the substantial center of the scan window.