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http://dx.doi.org/10.3745/KTSDE.2021.10.4.125

Line-Segment Feature Analysis Algorithm for Handwritten-Digits Data Reduction  

Kim, Chang-Min (상지대학교 컴퓨터공학과)
Lee, Woo-Beom (상지대학교 정보통신소프트웨어공학과)
Publication Information
KIPS Transactions on Software and Data Engineering / v.10, no.4, 2021 , pp. 125-132 More about this Journal
Abstract
As the layers of artificial neural network deepens, and the dimension of data used as an input increases, there is a problem of high arithmetic operation requiring a lot of arithmetic operation at a high speed in the learning and recognition of the neural network (NN). Thus, this study proposes a data dimensionality reduction method to reduce the dimension of the input data in the NN. The proposed Line-segment Feature Analysis (LFA) algorithm applies a gradient-based edge detection algorithm using median filters to analyze the line-segment features of the objects existing in an image. Concerning the extracted edge image, the eigenvalues corresponding to eight kinds of line-segment are calculated, using 3×3 or 5×5-sized detection filters consisting of the coefficient values, including [0, 1, 2, 4, 8, 16, 32, 64, and 128]. Two one-dimensional 256-sized data are produced, accumulating the same response values from the eigenvalue calculated with each detection filter, and the two data elements are added up. Two LFA256 data are merged to produce 512-sized LAF512 data. For the performance evaluation of the proposed LFA algorithm to reduce the data dimension for the recognition of handwritten numbers, as a result of a comparative experiment, using the PCA technique and AlexNet model, LFA256 and LFA512 showed a recognition performance respectively of 98.7% and 99%.
Keywords
MLP; Line-Segment Feature Analysis; Eigenvalue; Data Dimension Reduction; Handwritten-Digits Recognition;
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