Online vehicle marketplaces are embracing artificial intelligence to ease the process of selling a vehicle on their platform. The tedious work of copying information from the vehicle registration document into some web form can be automated with the help of smart text spotting systems. The seller takes a picture of the document and the necessary information is extracted automatically.
We introduce the components of a text spotting system including the subtasks of object detection and character object recognition (OCR). In view of our use case, we elaborate on the challenges of OCR in documents with various distortions and artefacts which rule out off-the-shelve products for this task.
After an introduction of semi-supervised learning based on generative adversarial networks (GANs), we evaluate the performance gains of this method compared to supervised learning. More specifically, for a varying amount of labelled data the accuracy of a convolution neural network (CNN) is compared to a GAN which uses additionally unlabelled data during the training phase.
We conclude that GANs significantly outperform classical CNNs in use cases with a lack of labelled data. Regarding our use case of extracting information from vehicle registration documents, we show that our text spotting system easily exceeds an accuracy of 99.5% thus making it applicable in a real-world use case.