A context-aware text recognition pipeline that groups and orders words in images and then applies a sequence-to-sequence correction model achieves 90% word accuracy on catalog images and 71% on protest sign images, beating a single-word baseline by roughly five points.
Grammar as a Foreign Language
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abstract
Syntactic constituency parsing is a fundamental problem in natural language processing and has been the subject of intensive research and engineering for decades. As a result, the most accurate parsers are domain specific, complex, and inefficient. In this paper we show that the domain agnostic attention-enhanced sequence-to-sequence model achieves state-of-the-art results on the most widely used syntactic constituency parsing dataset, when trained on a large synthetic corpus that was annotated using existing parsers. It also matches the performance of standard parsers when trained only on a small human-annotated dataset, which shows that this model is highly data-efficient, in contrast to sequence-to-sequence models without the attention mechanism. Our parser is also fast, processing over a hundred sentences per second with an unoptimized CPU implementation.
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Deep Neural Network for Semantic-based Text Recognition in Images
A context-aware text recognition pipeline that groups and orders words in images and then applies a sequence-to-sequence correction model achieves 90% word accuracy on catalog images and 71% on protest sign images, beating a single-word baseline by roughly five points.