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.

本文提出了一种名为 Multiplexed Multilingual Mask TextSpotter 的多路复用多语言 OCR 方法，该方法可以在字级别上执行脚本识别，并通过多个识别头来处理不同的脚本，同时维护统一的损失函数，从而实现脚本识别和多个识别头的优化，本方法具有较高的识别能力和多语言 OCR 系统的潜力。