  Recent advances in OCR have shown that an end-to-end (E2E) training pipeline
that includes both detection and recognition leads to the best results.
However, many existing methods focus primarily on Latin-alphabet languages,
often even only case-insensitive English characters. In this paper, we propose
an E2E approach, Multiplexed Multilingual Mask TextSpotter, that performs
script identification at the word level and handles different scripts with
different recognition heads, all while maintaining a unified loss that
simultaneously optimizes script identification and multiple recognition heads.
Experiments show that our method outperforms the single-head model with similar
number of parameters in end-to-end recognition tasks, and achieves
state-of-the-art results on MLT17 and MLT19 joint text detection and script
identification benchmarks. We believe that our work is a step towards the
end-to-end trainable and scalable multilingual multi-purpose OCR system. Our
code and model will be released.
