  More and more end-to-end text spotting methods based on Transformer
architecture have demonstrated superior performance. These methods utilize a
bipartite graph matching algorithm to perform one-to-one optimal matching
between predicted objects and actual objects. However, the instability of
bipartite graph matching can lead to inconsistent optimization targets, thereby
affecting the training performance of the model. Existing literature applies
denoising training to solve the problem of bipartite graph matching instability
in object detection tasks. Unfortunately, this denoising training method cannot
be directly applied to text spotting tasks, as these tasks need to perform
irregular shape detection tasks and more complex text recognition tasks than
classification. To address this issue, we propose a novel denoising training
method (DNTextSpotter) for arbitrary-shaped text spotting. Specifically, we
decompose the queries of the denoising part into noised positional queries and
noised content queries. We use the four Bezier control points of the Bezier
center curve to generate the noised positional queries. For the noised content
queries, considering that the output of the text in a fixed positional order is
not conducive to aligning position with content, we employ a masked character
sliding method to initialize noised content queries, thereby assisting in the
alignment of text content and position. To improve the model's perception of
the background, we further utilize an additional loss function for background
characters classification in the denoising training part.Although DNTextSpotter
is conceptually simple, it outperforms the state-of-the-art methods on four
benchmarks (Total-Text, SCUT-CTW1500, ICDAR15, and Inverse-Text), especially
yielding an improvement of 11.3% against the best approach in Inverse-Text
dataset.
