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Text recognition is a popular research subject with many associatedchallenges. Despite the considerable progress made in recent years, the textrecognition task itself is still constrained to solve the problem of readingcropped line text images and serves as a subtask of optical characterrecognition (OCR) systems. As a result, the final text recognition result islimited by the performance of the text detector. In this paper, we propose asimple, elegant and effective paradigm called Implicit Feature Alignment (IFA),which can be easily integrated into current text recognizers, resulting in anovel inference mechanism called IFAinference. This enables an ordinary textrecognizer to process multi-line text such that text detection can becompletely freed. Specifically, we integrate IFA into the two most prevailingtext recognition streams (attention-based and CTC-based) and proposeattention-guided dense prediction (ADP) and Extended CTC (ExCTC). Furthermore,the Wasserstein-based Hollow Aggregation Cross-Entropy (WH-ACE) is proposed tosuppress negative predictions to assist in training ADP and ExCTC. Weexperimentally demonstrate that IFA achieves state-of-the-art performance onend-to-end document recognition tasks while maintaining the fastest speed, andADP and ExCTC complement each other on the perspective of different applicationscenarios. Code will be available athttps://github.com/WangTianwei/Implicit-feature-alignment.

Implicit Feature Alignment: Learn to Convert Text Recognizer to Text Spotter