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<p><b>Abstract</b>—This paper presents a new approach to off-line, handwritten numeral recognition. From the concept of perturbation due to writing habits and instruments, we propose a recognition method which is able to account for a variety of distortions due to eccentric handwriting. We tested our method on two worldwide standard databases of isolated numerals, namely, CEDAR and NIST, and obtained 99.09 percent and 99.54 percent correct recognition rates at no-rejection level, respectively. The latter result was obtained by testing on more than 170,000 numerals.</p>
Writing habits and styles, writing instruments, reversing process, perturbation method, decision combination, k-nearest neighbor rule, neural networks.

H. Bunke and T. M. Ha, "Off-Line, Handwritten Numeral Recognition by Perturbation Method," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 19, no. , pp. 535-539, 1997.
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