High Precision License Plate Recognition Algorithm in Open Scene
At present,license plate recognition algorithm under restricted conditions is relatively mature and widely used in various license plate recognition system.Due to the influence of factors such as large differences in shooting angles and vehicle motion blur,Chinese license plate recognition is quite challenging.In response to the above problems,we abandoned the single end-to-end deep learning license plate recognition method,and proposed a step-by-step license plate recognition algorithm that integrated detection and classification,and utilized a level-by-level object detection strategy combined with character classification to predict the characters of the license plate result.On the basis,a multi-anchor character position regression algorithm was proposed to further accurately regress the local area position information of all license plate characters.At the same time,in order to meet the needs of character detection and character classification,and solve the problem of the imbalance of the existing license plate datasets,we contributed a series of supporting license plate datasets.Extensive experiments show that the proposed method can reach the current state-of-the-art on different datasets,and the accuracy rate on open dataset CCPD is up to 99%,with high precision and robustness in open scene.
Chinese license platelicense plate datasetlicense plate recognitioncharacter classificationmulti-anchor
万方数据
维普
