![]() Pattern Analysis and Applications, 16(3), 431–446.īissacco, A., Cummins, M., Netzer, Y., & Neven, H. Detection of artificial and scene text in images and video frames. In International conference on learning representations.Īnthimopoulos, M., Gatos, B., & Pratikakis, I. End-to-end text recognition with hybrid HMM maxout models. ![]() IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(12), 2552–2566. Word spotting and recognition with embedded attributes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(11), 2189–2202.Īlmazán, J., Gordo, A., Fornés, A., & Valveny, E. Measuring the objectness of image windows. Finally, we demonstrate a real-world application of our text spotting system to allow thousands of hours of news footage to be instantly searchable via a text query.Īlexe, B., Deselaers, T., & Ferrari, V. We perform rigorous experiments across a number of standard end-to-end text spotting benchmarks and text-based image retrieval datasets, showing a large improvement over all previous methods. Analysing the stages of our pipeline, we show state-of-the-art performance throughout. These networks are trained solely on data produced by a synthetic text generation engine, requiring no human labelled data. For the recognition and ranking of proposals, we train very large convolutional neural networks to perform word recognition on the whole proposal region at the same time, departing from the character classifier based systems of the past. Our pipeline uses a novel combination of complementary proposal generation techniques to ensure high recall, and a fast subsequent filtering stage for improving precision. This system is based on a region proposal mechanism for detection and deep convolutional neural networks for recognition. In this work we present an end-to-end system for text spotting-localising and recognising text in natural scene images-and text based image retrieval.
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