CHARACTER RECOGNITION METHOD

摘要

The present invention relates to a character recognition method. Same comprises: step 1: reading in a character image; step 2: image preprocessing; step 3: character splitting; step 4: edge extraction; utilizing an edge detection operator to detect edge points of characters; step 5: feature extraction, where the features of each edge point of each character are all expressed with distances running from other edge points of the character to the edge point and are represented by eigenvectors (Pk, Pk, . . . P); step 6: feature processing, mapping the eigenvectors (Pk, Pk, . . . P) into a matrix, T, thus allowing all characters to be identical in eigenvector dimension; and, step 7: template matching recognition. Because the features of each edge point of the characters are all expressed with the distances running from the other edge points to the edge point, the entirety of the features are ensured for the characters, and the difference in features are expanded for different characters, thus increasing character recognition accuracy.

主权项

1. A character recognition method, comprising:
step 1 comprising: reading a character image; step 2 comprising: image preprocessing; step 3 comprising: character segmentation; step 4 comprising: edge extraction, i.e., detecting edge points of the character by using an edge detecting operator; step 5 comprising: feature extraction, i.e., expressing features of each edge point of each character by distances from rest edge points of the character to the edge point, wherein to the features of the edge point of the character are represented by an eigenvector (P1k, P2k . . . PMk), thus eigenvectors of features of the character are represented by a matrix(P21P31…PM1P12`P32…PM2⋮⋮⋱⋮P1MP2M…P(M-1)M); step 6 comprising: feature processing, i.e., mapping the eigenvector (P1k, P2k . . . PMk) to a matrix T, to obtain a same dimension for eigenvectors of all characters, whereinT=(t11t12…t1vt21t22…t2v⋮⋮⋱⋮tu1tu2…tuv);and
step 7 comprising: template matching, i.e., assuming that an edge of a character Θ to be recognized has M points X1, X2 . . . XM, and a standard character template Δ has N points Y1, Y2 . . . YN, calculating a distance between the point Xi and the point Yj in the standard character template Δ by an equation as:Dij≡D(Xi,Yj)=∑m=1u∑n=1v(Ti(m,n)-Tj(m,n))2; denoting Diπ(i)=min Dij, which indicates that a point Yπ(i) in the standard character template Δ is a best matching point for the point Xi, a total distance from the character Θ to be recognized to the standard character template Δ beingDΘΔ=∑i=0MDiπ(i);and
in a case that there are total Γ standard character templates, calculating total distances from the character Θ to be recognized to each standard character template respectively, and a template with a minimal distance to the character is a recognition result for the character.