  Text recognition is a popular research subject with many associated
challenges. Despite the considerable progress made in recent years, the text
recognition task itself is still constrained to solve the problem of reading
cropped line text images and serves as a subtask of optical character
recognition (OCR) systems. As a result, the final text recognition result is
limited by the performance of the text detector. In this paper, we propose a
simple, elegant and effective paradigm called Implicit Feature Alignment (IFA),
which can be easily integrated into current text recognizers, resulting in a
novel inference mechanism called IFAinference. This enables an ordinary text
recognizer to process multi-line text such that text detection can be
completely freed. Specifically, we integrate IFA into the two most prevailing
text recognition streams (attention-based and CTC-based) and propose
attention-guided dense prediction (ADP) and Extended CTC (ExCTC). Furthermore,
the Wasserstein-based Hollow Aggregation Cross-Entropy (WH-ACE) is proposed to
suppress negative predictions to assist in training ADP and ExCTC. We
experimentally demonstrate that IFA achieves state-of-the-art performance on
end-to-end document recognition tasks while maintaining the fastest speed, and
ADP and ExCTC complement each other on the perspective of different application
scenarios. Code will be available at
https://github.com/WangTianwei/Implicit-feature-alignment.
