Holistic recognition of low quality license plates by CNN using track annotated data

A Robust Real-Time Automatic License Plate Recognition Based on the YOLO Detector

“…Many authors only addressed part of the ALPR pipeline, e.g., LP detection [28,32,36] or character/LP recognition [33,37,38], or performed their experiments on private datasets [9,14,38], making it difficult to accurately evaluate the presented methods. Note that works focused on a single stage do not consider localization errors (i.e., correct but not so accurate detections) in earlier stages [10,33]. Such errors directly affect the recognition results.…”

“…In our tests, this simple rule was sufficient to distinguish LPs with one and two rows of characters even in cases where the LP is considerably inclined. We emphasize that segmentation-free approaches (e.g., [8][9][10]) cannot recognize LPs with two rows of characters, contrarily to YOLO-based approaches, which are better suited to recognize them thanks to YOLO's versatility and ability to learn general component features, regardless of their positions [18].…”

“…Once these ranges were established, new images were generated using random values within those ranges for each parameter. Figure 6: Examples of LP images generated using the data augmentation technique proposed in [10]. The images in the first row are the originals and the others were generated automatically.…”

“…For each dataset, we compared the proposed ALPR system with state-of-the-art methods that were evaluated using the same protocol as the one described in Section 4.2. In addition, our results are compared with those obtained by Sighthound [60] and OpenALPR [62], which are two commercial systems often used as baselines in the ALPR literature [7,8,10,11,17]. According to the authors, both systems are robust for the detection and recognition of LPs of different layouts.…”

“…segmentation, it has become common the use of segmentation-free approaches for LP recognition [8][9][10][11], as the character segmentation by itself is a challenging task that is prone to be influenced by uneven lighting conditions, shadows, and noise [12].…”