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From some files of images and labels obtained by applying the project presented at https://github.com/ashok426/Vehicle-number-plate-recognition-YOLOv5, the images of license plates are filtered through a threshold that allows a better recognition of the license plate numbers by pytesseract. On 05/23/2022, a new version is introduced. On 07/04/20…
Through the use of Contrast Limited Adaptive Histogram Equalization (CLAHE) filters, completed with otsu filters, a direct reading of car license plates with success rates above 70% and an acceptable time is achieved
An Editor to perform rotation,conversion of the color model,Histogram equalization,Histogram chart,Mean filtering,and converting to the binary color of an image.
From images of cars in which their license plates have been labeled, and passing filters, their recognition is attempted by pytesseract . As there is not a single filter that works for all the licensess, it is tried with several filters and The license plate number that has been detected the most times is assigned.
This projects reflects the 3D reconstruction of a protein aggregate, after a careful processing (filtering, segmentation, reconstruction) of a set of slices of a protein aggregate.
OCR from scratch using Chars74 Dataset: http://www.ee.surrey.ac.uk/CVSSP/demos/chars74k/ applied to the case of Spanish car license plates or any other with format NNNNAAA. The hit rate is lower than that achieved by pytesseract: in a test with 21 images, 12 hits are reached while with pytesseract the hits are 17.
Prewitt edge detector: gradient filter és nonmaxima-suppression (NMS), Thresholding algorithm by Otsu, Detection of circular object by edge detection and Hough transform for circles, Motion tracking of feature points and dense optical flow