Boosting car plate recognition systems performances with agile re-Training

In this work, we report an experimental study on an Automatic Licence Plate Recognition system developed and commercialized by a partner company, with the main goals of critically analysing the original system and of devising effective but minimally invasive design changes. From a scientific point of view, ours is an attempt of reducing the gap between the different experimental approaches in academia and industry. The system is organized in layers, with an initial car plate proposal step followed by a OCR step. To cope with the drawbacks of the pre-existing system, we inserted an intermediate CNN binary classification step to discriminate between plates and non plates independently from the OCR module. Our solution incorporates new data available from working installations, in a closed refinement loop. We evaluate the modified system on 8 different installations. With respect to the original performances, we obtained significant improvements with an impact on both false positive (-9.8%) and false negatives (-5%).