Abstract
Handwritten numeral recognition is about identifying Arabic numerals and various coding and statistical data which are composed by a few of special symbols. Feature extraction for improved recognition rate has a great influence, and effective classification can be characterized in that the feature is separability, reliability and independence. Characteristic dimension should as little as possible. To meet this requirement, classifier needs to combine various features to put them together. This article describes the extraction method of handwritten digits eigen values, based on the researches on features of several handwritten digits, discussing 6 kinds of features, they are Fourier switch features, stroke density features, contour features, projection features, the barycenter and barycenter distance feature, wide grid feature. Finally using inner and outer analogy method to select and filter the features.
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Qing, Z., He, X. (2013). Feature Extraction and Filter in Handwritten Numeral Recognition. In: Bian, F., Xie, Y., Cui, X., Zeng, Y. (eds) Geo-Informatics in Resource Management and Sustainable Ecosystem. Communications in Computer and Information Science, vol 398. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-45025-9_7
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DOI: https://doi.org/10.1007/978-3-642-45025-9_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-45024-2
Online ISBN: 978-3-642-45025-9
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