Method for determining attributes using neural network and fuzzy logic
First Claim
1. An attribute decision method comprising steps of:
- making a comparison between at least one feature value, selected from seven feature values, of an input pattern contained in an input image and membership functions determined from 37 standard patterns including alphanumeric characters of "0" to "9" and "A" to "Z" and a hyphen of "-" with respect to the at least one selected feature value and then obtaining output values of the membership functions for the individual standard patterns with respect to the input pattern,the seven feature values being, when the input pattern is a mesh pattern, vertical structure vector sums which are obtained by determining a number of white meshes counted along each column top to bottom and bottom to top until a first black mesh is encountered in data of the mesh pattern and by summing up their results,horizontal structure vector sums which are obtained by determining a number of white meshes counted along each row left to right and right to left until a first black mesh is encountered in the data of the mesh pattern and by summing up their results,up-down and left-right area differences which are obtained by determining differences in areas of black meshes between upper and lower halves and between left and right halves in the data of the mesh pattern, anda vertical cross number which is obtained by determining a number of times of crossing with black meshes counted along a center of columns in a vertical direction;
calculating a total value of the output value for the at least one feature value for each of the standard patterns; and
determining a standard pattern of the highest total value among the calculated total values and thus deciding one of the standard patterns which has the highest similarity to the input pattern.
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Abstract
An attribute decision method includes steps of making a comparison between one feature value, selected from seven feature values, of an input pattern contained in an input image and membership functions determined from 37 standard patterns including alphanumeric characters of "0" to "9" and "A" to "Z" and a hyphen of "-" with respect to the selected feature value and then obtaining output values of the membership functions for the individual standard patterns with respect to the input pattern. The seven feature values are, when the input pattern is a mesh pattern, vertical structure vector sums, horizontal structure vector sums, up-down and left-right area differences, and a vertical cross number. The method also includes steps of calculating a total value of the output value for the feature value for each of the standard patterns, and determining a standard pattern of the highest total value among the calculated total values and thus deciding one of the standard patterns which has the highest similarity to the input pattern.
15 Citations
26 Claims
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1. An attribute decision method comprising steps of:
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making a comparison between at least one feature value, selected from seven feature values, of an input pattern contained in an input image and membership functions determined from 37 standard patterns including alphanumeric characters of "0" to "9" and "A" to "Z" and a hyphen of "-" with respect to the at least one selected feature value and then obtaining output values of the membership functions for the individual standard patterns with respect to the input pattern, the seven feature values being, when the input pattern is a mesh pattern, vertical structure vector sums which are obtained by determining a number of white meshes counted along each column top to bottom and bottom to top until a first black mesh is encountered in data of the mesh pattern and by summing up their results, horizontal structure vector sums which are obtained by determining a number of white meshes counted along each row left to right and right to left until a first black mesh is encountered in the data of the mesh pattern and by summing up their results, up-down and left-right area differences which are obtained by determining differences in areas of black meshes between upper and lower halves and between left and right halves in the data of the mesh pattern, and a vertical cross number which is obtained by determining a number of times of crossing with black meshes counted along a center of columns in a vertical direction; calculating a total value of the output value for the at least one feature value for each of the standard patterns; and determining a standard pattern of the highest total value among the calculated total values and thus deciding one of the standard patterns which has the highest similarity to the input pattern. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. An attribute decision method comprising steps of:
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making a comparison between at least one feature value, selected from seven feature values, of an input pattern contained in an input image and membership functions determined from 26 standard patterns including alphabetic characters of "A" to "Z" with respect to the at least one selected feature value and then obtaining output values of the membership functions for the individual standard patterns with respect to the input pattern, the seven feature values being, when the input pattern is a mesh pattern, vertical structure vector sums which are obtained by determining a number of white meshes counted along each column top to bottom and bottom to top until a first black mesh is encountered in data of the mesh pattern and by summing up their results, horizontal structure vector sums which are obtained by determining a number of white meshes counted along each row left to right and right to left until a first black mesh is encountered in the data of the mesh pattern and by summing up their results, up-down and left-right area differences which are obtained by determining differences in areas of black meshes between upper and lower halves and between left and right halves in the data of the mesh pattern, and a vertical cross number which is obtained by determining a number of times of crossing with black meshes counted along a center of columns in a vertical direction; calculating a total value of the output value for the at least one feature value for each of the standard patterns; and determining a standard pattern of the highest total value among the calculated total values and thus deciding one of the standard patterns which has the highest similarity to the input pattern. - View Dependent Claims (10, 11, 12, 13, 14, 15, 16, 17)
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18. An attribute decision method comprising steps of:
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making a comparison between at least one feature value, selected from seven feature values, of an input pattern contained in an input image and membership functions determined from 10 standard patterns including numerical characters of "1" to "0" with respect to the at least one selected feature value and then obtaining output values of the membership functions for the individual standard patterns with respect to the input pattern, the seven feature values being, when the input pattern is a mesh pattern, vertical structure vector sums which are obtained by determining a number of white meshes counted along each column top to bottom and bottom to top until a first black mesh is encountered in data of the mesh pattern and by summing up their results, horizontal structure vector sums which are obtained by determining a number of white meshes counted along each row left to right and right to left until a first black mesh is encountered in the data of the mesh pattern and by summing up their results, up-down and left-right area differences which are obtained by determining differences in areas of black meshes between upper and lower halves and between left and right halves in the data of the mesh pattern, and a vertical cross number which is obtained by determining a number of times of crossing with black meshes counted along a center of columns in a vertical direction; calculating a total value of the output value for the at least one feature value for each of the standard patterns; and determining a standard pattern of the highest total value among the calculated total values and thus deciding one of the standard patterns which has the highest similarity to the input pattern. - View Dependent Claims (19, 20, 21, 22, 23, 24, 25, 26)
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Specification