| Summary: | 碩士 === 雲林科技大學 === 電機工程系碩士班 === 98 === Related researches and applications of License Plate Recognition (LPR) have been proceeding for decades. But, in practice, the license plate detection rate is usually error-prone to various backgrounds, illuminations, or skews. So is the subsequent character recognition rate. This is why most of the license plate recognition system just can work well under specific circumstances. Specifically, License plate detection is the fundamental step in LPR.
In general, the license plate region is usually detected by the strong vertical edge feature of its interior characters. However, in the external environment, many backgrounds always have strong vertical edges as the license plate. This paper focuses on enlarging the vertical edge differentation between the license plate region and the non-plate region, that is, focuses on enhancing the vertical edge density and strength of the license plate region and weakening those of the non-plate region, e.g. weakening those of the grille. Therefore, the deblurred method of Wiener deconvolution is proposed to enhance and increase the vertical edges of the license plate. Then, 2-level 2D Discrete Wavelet Transform (DWT) is adopted to run the projection histogram of vertical edges. Furthermore, with the first-order local recursive Otsu segmentation, mathematical morphology, and edge density verification method, the license plate region can be detected smoothly. Experimental results show LPD system based on the proposed Wiener-deconvolution vertical edge enhancement method and 2-level 2D DWT, can achieve a much higher hit rate. After extracting the license plate region smoothly, the optical character recognition library of Tesseract OCR can be applied to recognize the license plate characters. On the other hand, this thesis implements a LPR with the Wiener-like vertical edge enhancement method onto Android embedded platform. The implementation result also verifies the Wiener-like vertical edge enhancement method is effective and feasible.
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