Character and pattern recognition machine and method

Claims

What is claimed is:

1. In a hand print character recognition system comprising,

(a) means for creating an electrical binary black/white "image" of one or more hand printed characters,

(b) means for selecting a plurality of centers of recognition (CORs) within said binary black/white image as reference points and measuring the characteristic enclave of the black/white image immediately surrounding the CORs,

(c) means for storing a library of templates of said measurements around the CORs for a plurality of known exemplary character images,

(d) means for comparing said library of templates to corresponding measurements made around the CORs of images whose class is unknown to produce "template scores" proportional to the similarity of the enclaves of the known image to the enclaves measured by templates,

(e) means for expressing the generic shape of a character as being a "character equation" involving template scores developed on an unknown image, and

(f) means for evaluating each character equation, including comparing the values of such equations, and selecting the best value to determine the generic name of the unknown character.

2. The handprint character recognition system defined in claim 1 wherein said (b) means for selecting includes means for measuring the enclosure characteristic of each pixel within an enclave which is roughly related to the COR so each pixel has its own enclosure descriptions in four quadrants.

3. The handprint character recognition system defined in claim 1 wherein said handprint characters include one or more overlapping characters and said (c) means for storing a library of templates includes templates corresponding to said one or more overlapping characters.

4. The handprint character recognition system defined in claim 1, including means for choosing and manipulating measurement parameters such that very important characteristics, including the degree of black enclosure around the COR are normalized to be independent of relatively unimportant characteristics, such as size and distance of the black enclosure from the pixel or COR under consideration in the enclaves.

5. The handprint character recognition system defined in claim 4, including logic means for determining the boundedness of a white pixel in each of four quadrants (NE, NW, SE and SW) of said enclave, wherein the boundedness of a white pixel has a plurality of possible descriptions, including one or more of the following: the presence or absence of a black pixel on a vertical axis of said white pixel, the presence or absence of a black pixel on a selected diagonal of said wite pixel, and the presence or absence of a black pixel on a horizontal axis of said white pixel, the black pixel on the vertical axis being required to be above said white pixel for boundedness descriptions of a white pixel in said NE or NW quadrants, respectively, but being required to be below a white pixel in said SE or SW quadrants, the black pixel on said selected diagonal axis being required to be above and to the right of said white pixel for boundedness descriptions of a white pixel in said NE quadrant, below and to the right for said white pixel in said SE quadrant, below and to the left for said white pixel in said SW quadrant, above and to the left for said white pixel in said NW quadrant, the presence or absence of a black pixel on the horizontal axis of said white pixel, the black pixel being required to be to the right of said white pixel in said NE and SE quadrants, but being required to be to the left of said white pixel in said SW and NW quadrants.

6. The handprint character recognition system defined in claim 5, including means for counting pixels with similar boundedness and for normalizing their number by computing ratos of their number to the total number of white pixels in the enclave with which it is associated.

7. The handprint character recognition system defined in claim 5, including means for using said measurement parameters to choose the location for the centers of recognition, to locate templates themselves to recognize useful CORs.

8. The handprint character recognition system defined in claim 5, including means for relaxing said measurement parameters so as to use them to select centers of recognition which are less narrowly defined than the measurement parameters needed in the character equations.

9. In a hand print character recognition system comprising, means for transporting media bearing hand print characters, a photosensitive device, an optical system for focusing images of said hand print characters upon said photosensitive device, scanning means for converting the optical signals focused on said device to electrical signals, analog-to-digital converters for changing electrical grey scale levels associated with each individual small picture elements (pixels) in said image to digital values, and decision means for quantizing said pixels to be either black or white and creating a binary black/white "image" of a character or group of characters, the improvement comprising

(a) means for selecting a plurality of centers of recognition (CORs) within said binary black/white image as reference points for measurement of the characteristic enclave of the black/white image immediately surrounding the CORs,

(b) means for storing a library of templates of said measurements around the CORs for a plurality of known exemplary character images,

(c) means for comparing said library of templates to corresponding measurements made around the CORs of images whose class is unknown for producing "template scores" proportional to the similarity of the enclaves of the unknown image to the enclaves measured by the templates,

(d) means for expressing the generic shape of a character as being a "character equation" involving template scores developed on an unknown image, and

(e) means for evaluating each character equation, including comparing the values of such equations and selecting the character equation which matches the shape of the unknown image to determine the generic name of the unknown character.

10. The handprint character recognition system defined in claim 9 wherein said means for selecting includes means for measuring the enclosure characteristic of each pixel within an enclave .

11. The handprint character recognition system defined in claim 9 including means for choosing and manipulating measurement parameters such that very important characteristics, including the degree of black enclosure around the COR are normalized to be independent of relatively unimportant characteristics, such as size and distance of the black enclosure from the pixel or COR under consideration of the enclaves.

12. The handprint character recognition system defined in claim 11 including logic means for determining the boundedness of a white pixel in each of four quadrants (NE, NW, SE and SW) of said enclave, respectively, wherein the boundedness of a white pixel has a plurality of possible descriptions, including one or more of the following: the presence or absence of a black pixel on a vertical axis of said white pixel, the presence or absence of a black pixel on a selected diagonal of said white pixel, and the presence or absence of a black pixel on a horizontal axis of said white pixel, the black pixel on the vertical axis being required to be above said white pixel for boundedness descriptions of a white pixel in said NE or NW quadrants, respectively, but being required to be below a white pixel in said SE or SW quadrants, the black pixel on said selected diagonal axis being required to be above and the right of said white pixel for boundedness descriptions of a white pixel in said NE quadrant, below and to the right for a white pixel in said SE quadrant, below and to the left for a white pixel in said SW quadrant, above and to the left for a white pixel in said NW quadrant, the presence or absence of a black pixel on the horizontal axis of said white pixel, the black pixel being required to be to the right of said white pixel in said NE and SE quadrants, but being required to be to the left of said white pixel in said SW and NW quadrants.

13. The handprint character recognition system defined in claim 12 including means for counting pixels with similar boundedness and for normalizing their number by computing ratios of their number to the total number of white pixels in the enclave with which it is associated.

14. The handprint character recognition system defined in claim 12 including means for using said measurement parameters to choose the location for the centers of recognition, to locate templates themselves to recognize useful CORs.

15. The handprint character recognition system defined in claim 12 including means for relaxing said measurement parameters so as to use them to select centers of recognition which are less narrowly defined than the measurement parameters needed in the character equations.

16. The handprint recognition system defined in claim 1 including means for determining whether a selected pixel is a member of a selected enclave.

17. In a hand print character recognition method wherein an image of a character is converted to an electrical binary black/white "image" of a character or group of characters is the improvement comprising,

(a) selecting a plurality of centers of recognition (CORs) within said binary black/white image as reference points and measuring the characteristic enclave of the black/white image immediately surrounding the CORs,

(b) storing a library of templates of said measurements around the CORs for a plurality of known exemplary character images,

(c) comparing said library of templates to corresponding measurements made around the CORs of images whose class is unknown to produce "template scores" proportional to the similarity of the enclaves of the known image to the enclaves measured by templates,

(d) expressing the generic shape of a character as being a "character equation" involving template scores developed on an unknown image, and

(e) evaluating each character equation, including comparing the values of such equations, and selecting the best value to determine the generic name of the unknown character.

18. The handprint character recognition method defined in claim 17 wherein said step (a) selecting includes measuring the enclosure characteristic of each pixel within an encalve so each pixel has its own enclos