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E2E-MLT - an Unconstrained End-to-End Method for Multi-Language Scene Text

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arxiv 1801.09919 v2 pith:TFQC7XPX submitted 2018-01-30 cs.CV

Michal Bušta , Yash Patel , Jiri Matas This is my paper · ORCID
classification cs.CV
keywords multi-languagescenetexte2e-mltend-to-endfullymethodtrained
verification ladder T0 review T1 audit T2 compute T3 formal
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An end-to-end trainable (fully differentiable) method for multi-language scene text localization and recognition is proposed. The approach is based on a single fully convolutional network (FCN) with shared layers for both tasks. E2E-MLT is the first published multi-language OCR for scene text. While trained in multi-language setup, E2E-MLT demonstrates competitive performance when compared to other methods trained for English scene text alone. The experiments show that obtaining accurate multi-language multi-script annotations is a challenging problem.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Towards Unconstrained End-to-End Text Spotting

    cs.CV 2019-08 conditional novelty 7.0 of 10

    A Mask R-CNN and attention-based text spotter handles curved text by masking RoI features instead of rectifying them, and uses OCR-engine labels as extra training data to set state-of-the-art results on ICDAR15 and To...

  2. Mask TextSpotter: An End-to-End Trainable Neural Network for Spotting Text with Arbitrary Shapes

    cs.CV 2019-08 conditional novelty 5.0 of 10

    An end-to-end neural network detects and recognizes arbitrary-shape scene text using instance segmentation, character segmentation, and spatial attention, setting state-of-the-art results on several benchmarks.

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