# Implicit Feature Alignment: Learn to Convert Text Recognizer to Text Spotter arXiv: https://arxiv.org/abs/2106.05920 ยท HTML: https://mlsanity.com/paper/2106.05920/ ## Key facts - Cited by 5 papers in the map (data through 14 Oct 2026). - Builds on 33 of its 57 references that are in the map. - First posted on arXiv 10 Jun 2021. ## Builds on (most cited first, 30 of 33) - [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015) - [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015) - [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014) - [Effective Approaches to Attention-based Neural Machine Translation](https://mlsanity.com/paper/1508.04025/) (2015) - [An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition](https://mlsanity.com/paper/1507.05717/) (2015) - [Synthetic Data for Text Localisation in Natural Images](https://mlsanity.com/paper/1604.06646/) (2016) - [Synthetic Data and Artificial Neural Networks for Natural Scene Text Recognition](https://mlsanity.com/paper/1406.2227/) (2014) - [Arbitrary-Oriented Scene Text Detection via Rotation Proposals](https://mlsanity.com/paper/1703.01086/) (2017) - [Mask TextSpotter: An End-to-End Trainable Neural Network for Spotting Text with Arbitrary Shapes](https://mlsanity.com/paper/1908.08207/) (2019) - [ABCNet: Real-time Scene Text Spotting with Adaptive Bezier-Curve Network](https://mlsanity.com/paper/2002.10200/) (2020) - [Towards Accurate Scene Text Recognition with Semantic Reasoning Networks](https://mlsanity.com/paper/2003.12294/) (2020) - [Show, Attend and Read: A Simple and Strong Baseline for Irregular Text Recognition](https://mlsanity.com/paper/1811.00751/) (2018) - [Robust Scene Text Recognition with Automatic Rectification](https://mlsanity.com/paper/1603.03915/) (2016) - [SEED: Semantics Enhanced Encoder-Decoder Framework for Scene Text Recognition](https://mlsanity.com/paper/2005.10977/) (2020) - [FOTS: Fast Oriented Text Spotting with a Unified Network](https://mlsanity.com/paper/1801.01671/) (2018) - [Decoupled Attention Network for Text Recognition](https://mlsanity.com/paper/1912.10205/) (2019) - [ESIR: End-to-end Scene Text Recognition via Iterative Image Rectification](https://mlsanity.com/paper/1812.05824/) (2018) - [Recursive Recurrent Nets with Attention Modeling for OCR in the Wild](https://mlsanity.com/paper/1603.03101/) (2016) - [SCATTER: Selective Context Attentional Scene Text Recognizer](https://mlsanity.com/paper/2003.11288/) (2020) - [TextScanner: Reading Characters in Order for Robust Scene Text Recognition](https://mlsanity.com/paper/1912.12422/) (2019) - [All You Need Is Boundary: Toward Arbitrary-Shaped Text Spotting](https://mlsanity.com/paper/1911.09550/) (2019) - [Scene Text Recognition from Two-Dimensional Perspective](https://mlsanity.com/paper/1809.06508/) (2018) - [Towards Unconstrained End-to-End Text Spotting](https://mlsanity.com/paper/1908.09231/) (2019) - [Towards End-to-end Text Spotting with Convolutional Recurrent Neural Networks](https://mlsanity.com/paper/1707.03985/) (2017) - [OrigamiNet: Weakly-Supervised, Segmentation-Free, One-Step, Full Page Text Recognition by learning to unfold](https://mlsanity.com/paper/2006.07491/) (2020) - [Text Perceptron: Towards End-to-End Arbitrary-Shaped Text Spotting](https://mlsanity.com/paper/2002.06820/) (2020) - [Joint Line Segmentation and Transcription for End-to-End Handwritten Paragraph Recognition](https://mlsanity.com/paper/1604.08352/) (2016) - [Symmetry-constrained Rectification Network for Scene Text Recognition](https://mlsanity.com/paper/1908.01957/) (2019) - [Verisimilar Image Synthesis for Accurate Detection and Recognition of Texts in Scenes](https://mlsanity.com/paper/1807.03021/) (2018) - [UnrealText: Synthesizing Realistic Scene Text Images from the Unreal World](https://mlsanity.com/paper/2003.10608/) (2020) ## Built on by (most cited first, 5 of 5) - [LISTER: Neighbor Decoding for Length-Insensitive Scene Text Recognition](https://mlsanity.com/paper/2308.12774/) (2023) - [Visual-Semantic Transformer for Scene Text Recognition](https://mlsanity.com/paper/2112.00948/) (2021) - [TextBlockV2: Towards Precise-Detection-Free Scene Text Spotting with Pre-trained Language Model](https://mlsanity.com/paper/2403.10047/) (2024) - [DA-Mamba: Domain Adaptive Hybrid Mamba-Transformer Based One-Stage Object Detection](https://mlsanity.com/paper/2502.11178/) (2025) - [Scene Text Recognition with Single-Point Decoding Network](https://mlsanity.com/paper/2209.01914/) (2022) ## Abstract (arXiv) 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.