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

نویسندگان

  • Yash Patel
  • Michal Busta
  • Jiri Matas
چکیده

An end-to-end method for multi-language scene text localization, recognition and script identification is proposed. The approach is based on a set of convolutional neural nets. The method, called E2E-MLT, achieves state-of-theart performance for both joint localization and script identification in natural images and in cropped word script identification. E2E-MLT is the first published multi-language OCR for scene text. The experiments show that obtaining accurate multi-language multiscript annotations is a challenging problem.

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عنوان ژورنال:
  • CoRR

دوره abs/1801.09919  شماره 

صفحات  -

تاریخ انتشار 2018