The pursuit of high performance on public benchmarks has been the driving
force for research in scene text recognition, and notable progress has been
achieved. However, a close investigation reveals a startling fact that the
state-of-the-art methods perform well on images with words within vocabulary
but generalize poorly to images with words outside vocabulary. We call this
phenomenon "vocabulary reliance". In this paper, we establish an analytical
framework to conduct an in-depth study on the problem of vocabulary reliance in
scene text recognition. Key findings include: (1) Vocabulary reliance is
ubiquitous, i.e., all existing algorithms more or less exhibit such
characteristic; (2) Attention-based decoders prove weak in generalizing to
words outside vocabulary and segmentation-based decoders perform well in
utilizing visual features; (3) Context modeling is highly coupled with the
prediction layers. These findings provide new insights and can benefit future
research in scene text recognition. Furthermore, we propose a simple yet
effective mutual learning strategy to allow models of two families
(attention-based and segmentation-based) to learn collaboratively. This remedy
alleviates the problem of vocabulary reliance and improves the overall scene
text recognition performance.

本文研究场景文本识别中的 “词汇依赖” 现象，发现现有算法普遍存在这种特征，提出基于注意力和分割的模型在处理词汇外的文本识别时表现良好，同时提出了一种名为 “mutual learning” 的简单而有效的模型训练策略，这种方法成功缓解了词汇依赖问题，并提高了场景文本识别的性能。