@inproceedings{wenzel2017towards,
    type = {inproceedings},
    key = {wenzel2017towards},
    title = {Towards Unconstrained Content Recognition of Additional Traffic Signs},
    author = {Thomas Wenzel and Steffen Brueggert and Joachim Denzler},
    booktitle = {IEEE Intelligent Vehicles Symposium (IV)},
    year = {2017},
    month = {June},
    pages = {1421-1427},
    abstract = {The task of traffic sign recognition is often considered to be solved after almost perfect results have been achieved on some public benchmarks. Yet, the closely related recognition of additional traffic signs is still lacking a solution. Following up on our earlier work on detecting additional traffic signs given a main sign detection [1], we here propose a complete pipeline for recognizing the content of additional signs, including text recognition by optical character recognition (OCR). We assume a given additional sign detection, first classify its layout, then determine content bounding boxes by regression, followed by a multi-class classification step or, if necessary, OCR by applying a text sequence classifier. We evaluate the individual stages of our proposed pipeline and the complete system on a database of German additional signs and show that it can successfully recognize about 80% of the signs correctly, even under very difficult conditions and despite low input resolutions at runtimes well below 12ms per sign.},
    doi = {10.1109/IVS.2017.7995909},
    keywords = {image classification;object recognition;optical character recognition;regression analysis;text detection;traffic engineering computing;visual databases;unconstrained content recognition;traffic sign recognition;public benchmarks;traffic sign detection;main sign detection;text recognition;optical character recognition;OCR;layout classification;content bounding boxes;regression;multiclass classification step;text sequence classifier;German additional sign database;Layout;Optical character recognition software;Text recognition;Databases;Pipelines;Training;Standards},
    url = {https://ieeexplore.ieee.org/document/7995909},
}