Bridging the Gap Between End-to-End and Two-Step Text Spotting
Mingxin Huang Hongliang Li Yuliang Liu Xiang Bai Lianwen Jin
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 https://github.com/mxin262/Bridging-Text-Spotting.