E2E-MLT - An Unconstrained End-to-End Method for Multi-language Scene Text

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. Code and trained models are released publicly at https://github.com/MichalBusta/E2E-MLT.