License Plate Segmentation in Images Based on per-Block Contrast Analysis and CCA, in

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ALPR based applications are more and more used today. Besides the OCR part, the vehicle registration plate detection in real world images represents the main challenge in LPR. This paper presents a simple, yet quite general, fast and effective method for license plate (LP) segmentation. It is based on the evaluation of a local contrast (high gradient) measure at the level of image blocks, binarization of the downscaled contrast map obtained with these values, and analysis of connectivity between its runs, requiring modest CPU and memory resources. It provides as output not only the locations of detected LPs, but also associated bitmaps, black on white, containing only their constituent alphanumeric characters, aligned horizontally, with no slope, slant or tilt, and free of other parasitic noise. Such black on white bitmaps are directly suitable for further OCR, the correctness and completeness of final LPR strongly depending on the quality of the bitmap provided. Extended experiments carried out on own image set, as well as on other (public) data sets, showed good performance and results of the implemented method in the vast majority of situations, even on certain difficult, poor quality images. Comparison with state-of-the-art (based on deep neural networks, and high-end GPU parallel computing), also proved average good performance on public data sets complying with the minimal requirements of our method.
Computer vision (CV), Segmentation, License plate detection (LPD), License plate recognition (LPR), Optical character recognition (OCR), Histogram, Threshold, Binarization, Bitmap, Run, Connected component analysis (CCA)