Modularity plays a crucial role in the development and maintenance of complex
systems. While end-to-end text spotting efficiently mitigates the issues of
error accumulation and sub-optimal performance seen in traditional two-step
methodologies, the two-step methods continue to be favored in many competitions
and practical settings due to their superior modularity. In this paper, we
introduce Bridging Text Spotting, a novel approach that resolves the error
accumulation and suboptimal performance issues in two-step methods while
retaining modularity. To achieve this, we adopt a well-trained detector and
recognizer that are developed and trained independently and then lock their
parameters to preserve their already acquired capabilities. Subsequently, we
introduce a Bridge that connects the locked detector and recognizer through a
zero-initialized neural network. This zero-initialized neural network,
initialized with weights set to zeros, ensures seamless integration of the
large receptive field features in detection into the locked recognizer.
Furthermore, since the fixed detector and recognizer cannot naturally acquire
end-to-end optimization features, we adopt the Adapter to facilitate their
efficient learning of these features. We demonstrate the effectiveness of the
proposed method through extensive experiments: Connecting the latest detector
and recognizer through Bridging Text Spotting, we achieved an accuracy of 83.3%
on Total-Text, 69.8% on CTW1500, and 89.5% on ICDAR 2015. The code is available
at this https URL

通过引入 Bridging Text Spotting 方法，本文解决了两步方法中存在的错误累积和次优性能问题，同时保留了模块化特性。通过将训练良好的检测器和识别器锁定参数，并通过初始化权重为零的神经网络连接它们，确保了检测中的大感受野特征自然地融入锁定的识别器中。此外，为了有效学习端到端优化特性，采用了 Adapter。通过广泛的实验证明了该方法的有效性。