Existing scene text spotting (i.e., end-to-end text detection and
recognition) methods rely on costly bounding box annotations (e.g., text-line,
word-level, or character-level bounding boxes). For the first time, we
demonstrate that training scene text spotting models can be achieved with an
extremely low-cost annotation of a single-point for each instance. We propose
an end-to-end scene text spotting method that tackles scene text spotting as a
sequence prediction task. Given an image as input, we formulate the desired
detection and recognition results as a sequence of discrete tokens and use an
auto-regressive Transformer to predict the sequence. The proposed method is
simple yet effective, which can achieve state-of-the-art results on widely used
benchmarks. Most significantly, we show that the performance is not very
sensitive to the positions of the point annotation, meaning that it can be much
easier to be annotated or even be automatically generated than the bounding box
that requires precise positions. We believe that such a pioneer attempt
indicates a significant opportunity for scene text spotting applications of a
much larger scale than previously possible. The code is available at
this https URL

提出了一种新的场景文本识别方法，使用单点标注替代昂贵的边界框标注，将场景文本定位和识别视为序列预测任务，并使用自回归 Transformer 模型预测序列，可达到最先进的识别效果。