EDF‐LPR: a new encoder–decoder framework for license plate recognition

Abstract

Although automatic license plate recognition (ALPR) has been studied for decades, the final recognition result can be accurate only if the license plate is detected and the standard format is unambiguous. However, since an image may contain license plates with different formats and scales, license plate detection and standard format classification may fail. In this study, a new ALPR codec framework named EDF-LPR is presented. As for the encoder, at the first stage, candidate license plate characters are detected and recognised directly without considering the format of license plate, and candidate regions of characters are extracted by density-based spatial clustering of applications with noise-like algorithm; at the second stage, poor regions are processed by tilt correction and scale normalisation to obtain more accurate candidate characters. As for the decoder, a sequence learning model is trained to convert each unordered coded sequence into a sequence composed of marks that indicate a way to construct the final result string. Experiments are designed to evaluate the performance of EDF-LPR on both detection rate and recognition rate. The experimental results on public datasets show that the detection rate and recognition rate are 99.51 and 95.3%, respectively, at about 40 fps.