By Professor Dr. Zhi-Qiang Liu, Dr. Jinhai Cai, Richard Buse (auth.)
Over the previous couple of a long time, study on handwriting reputation has made remarkable growth. The study and improvement on handwritten notice reputation are to a wide measure influenced via many program components, similar to computerized postal tackle and code studying, facts acquisition in banks, text-voice conversion, safety, and so on. because the costs of scanners, com puters and handwriting-input units are falling progressively, we've seen an elevated call for for handwriting popularity platforms and software program pack a while. a few advertisement handwriting reputation platforms at the moment are in the market. present advertisement platforms have a powerful functionality in spotting machine-printed characters and smartly written texts. For in stance, High-Tech ideas in Israel has constructed a number of items for box identity reputation, vehicle registration code acceptance and package deal label attractiveness. Xerox within the U. S. has constructed TextBridge for changing hardcopy records into digital rfile documents. inspite of the remarkable development, there's nonetheless an important perfor mance hole among the human and the desktop in spotting off-line unconstrained handwritten characters and phrases. The problems encoun tered in spotting unconstrained handwritings are regularly because of large adaptations in writing types and the overlapping and the interconnection of neighboring characters. additionally, many functions call for very excessive reputation accuracy and reliability. for instance, within the banking region, even if automatic teller machines (ATMs) and networked banking sys tems at the moment are generally to be had, many transactions are nonetheless conducted within the type of cheques.
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Extra info for Handwriting Recognition: Soft Computing and Probabilistic Approaches
7. 16. ". 40) 44 2. Pre-processing and Feature Extraction Outgi(Xi, Yi)r, = L L gi(X, Y)r,Z(X x Xi, Y - Yi)dxdy. 41) y In practice, the range of summation is quite small. 5ax without losing much accuracy, where r = J(x - Xi)2 + (y - Yi)2. The responses of the Gabor filter are robust to noise, because they depend on all pixels over the kernel. 17(C). In addition, a larger value of Outgr(Xi, Yi)r, means that there is a higher probability that the pixel at (Xi, Yi) is a ridge point of a line with orientation 7]7r/8, and a larger value of IOutgi(Xi,Yi)r,1 means that there is a higher probability that the pixel is an edge point of the line.
24 shows some feature extraction results for gray-scale images using Lee and Kim's method 2 . The images in the left column are results of Lee and Kim's method 2 and the images in the right column are results after deleting redundant data. 24(a) to (f) are obtained from good quality gray-scale images by Lee and Kim's method. However, their method produces the skeletons with lots of noisy branches and short lines. Their method is more likely to break lines, specially, at crosses. 24(g) and (h) are examples produced by Lee and Kim's method for noisy image.
22. Feature extraction from cursive writings. small holes, and significant distortions. For the human, however, it is not difficult to identify the line-segments caused by noise. Our method is quite successful in dealing with such low quality images. 23(c) shows the orientation strength functions at optimal directions, where the noisy lines caused by the writing tool have been erased. This property is attributed to the Gabor filter whose parameters have been automatically adjusted using the methods presented in the previous sections to adapt to the average line width.
Handwriting Recognition: Soft Computing and Probabilistic Approaches by Professor Dr. Zhi-Qiang Liu, Dr. Jinhai Cai, Richard Buse (auth.)