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arXiv · 1801.09919

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

Michal Bušta·Yash Patel·Jiri Matas
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Abstract

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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Michal Bušta, Yash Patel, Jiri Matas. 2018-01-30. E2E-MLT - an Unconstrained End-to-End Method for Multi-Language Scene Text. https://arxiv.org/abs/1801.09919

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