{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/an-end-to-end-trainable-neural-network-for","title":"An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition","arxiv_id":"1507.05717","date":"2015-07-21","proceeding":null,"authors":["Baoguang Shi","Xiang Bai","Cong Yao"],"abstract":"Image-based sequence recognition has been a long-standing research topic in\ncomputer vision. In this paper, we investigate the problem of scene text\nrecognition, which is among the most important and challenging tasks in\nimage-based sequence recognition. A novel neural network architecture, which\nintegrates feature extraction, sequence modeling and transcription into a\nunified framework, is proposed. Compared with previous systems for scene text\nrecognition, the proposed architecture possesses four distinctive properties:\n(1) It is end-to-end trainable, in contrast to most of the existing algorithms\nwhose components are separately trained and tuned. (2) It naturally handles\nsequences in arbitrary lengths, involving no character segmentation or\nhorizontal scale normalization. (3) It is not confined to any predefined\nlexicon and achieves remarkable performances in both lexicon-free and\nlexicon-based scene text recognition tasks. (4) It generates an effective yet\nmuch smaller model, which is more practical for real-world application\nscenarios. The experiments on standard benchmarks, including the IIIT-5K,\nStreet View Text and ICDAR datasets, demonstrate the superiority of the\nproposed algorithm over the prior arts. Moreover, the proposed algorithm\nperforms well in the task of image-based music score recognition, which\nevidently verifies the generality of it.","url_abs":"http://arxiv.org/abs/1507.05717v1","url_pdf":"http://arxiv.org/pdf/1507.05717v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 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