# Scene Text recognition with Full Normalization

arXiv: https://arxiv.org/abs/2109.01034 · HTML: https://mlsanity.com/paper/2109.01034/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 31 of its 44 references that are in the map.
- First posted on arXiv 13 Jul 2021.

## Builds on (most cited first, 30 of 31)

- [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)
- [EAST: An Efficient and Accurate Scene Text Detector](https://mlsanity.com/paper/1704.03155/) (2017)
- [Synthetic Data and Artificial Neural Networks for Natural Scene Text Recognition](https://mlsanity.com/paper/1406.2227/) (2014)
- [Mask TextSpotter: An End-to-End Trainable Neural Network for Spotting Text with Arbitrary Shapes](https://mlsanity.com/paper/1908.08207/) (2019)
- [Total-Text: A Comprehensive Dataset for Scene Text Detection and Recognition](https://mlsanity.com/paper/1710.10400/) (2017)
- [Reading Text in the Wild with Convolutional Neural Networks](https://mlsanity.com/paper/1412.1842/) (2014)
- [Robust Scene Text Recognition with Automatic Rectification](https://mlsanity.com/paper/1603.03915/) (2016)
- [TextBoxes: A Fast Text Detector with a Single Deep Neural Network](https://mlsanity.com/paper/1611.06779/) (2016)
- [TextSnake: A Flexible Representation for Detecting Text of Arbitrary Shapes](https://mlsanity.com/paper/1807.01544/) (2018)
- [Focusing Attention: Towards Accurate Text Recognition in Natural Images](https://mlsanity.com/paper/1709.02054/) (2017)
- [FOTS: Fast Oriented Text Spotting with a Unified Network](https://mlsanity.com/paper/1801.01671/) (2018)
- [ICDAR2017 Competition on Reading Chinese Text in the Wild (RCTW-17)](https://mlsanity.com/paper/1708.09585/) (2017)
- [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)
- [Spatial Fusion GAN for Image Synthesis](https://mlsanity.com/paper/1812.05840/) (2018)
- [PixelLink: Detecting Scene Text via Instance Segmentation](https://mlsanity.com/paper/1801.01315/) (2018)
- [Verisimilar Image Synthesis for Accurate Detection and Recognition of Texts in Scenes](https://mlsanity.com/paper/1807.03021/) (2018)
- [Multi-Oriented Scene Text Detection via Corner Localization and Region Segmentation](https://mlsanity.com/paper/1802.08948/) (2018)
- [GA-DAN: Geometry-Aware Domain Adaptation Network for Scene Text Detection and Recognition](https://mlsanity.com/paper/1907.09653/) (2019)
- [Multi-Oriented Text Detection with Fully Convolutional Networks](https://mlsanity.com/paper/1604.04018/) (2016)
- [Deep Matching Prior Network: Toward Tighter Multi-oriented Text Detection](https://mlsanity.com/paper/1703.01425/) (2017)
- [Single Shot Text Detector with Regional Attention](https://mlsanity.com/paper/1709.00138/) (2017)
- [Accurate Scene Text Detection through Border Semantics Awareness and Bootstrapping](https://mlsanity.com/paper/1807.03547/) (2018)
- [WordSup: Exploiting Word Annotations for Character based Text Detection](https://mlsanity.com/paper/1708.06720/) (2017)
- [Scene Text Synthesis for Efficient and Effective Deep Network Training](https://mlsanity.com/paper/1901.09193/) (2019)
- [Deep Structured Output Learning for Unconstrained Text Recognition](https://mlsanity.com/paper/1412.5903/) (2014)
- [Spatial-Aware GAN for Unsupervised Person Re-identification](https://mlsanity.com/paper/1911.11312/) (2019)
- [Text-Attentional Convolutional Neural Networks for Scene Text Detection](https://mlsanity.com/paper/1510.03283/) (2015)
- [Towards Realistic 3D Embedding via View Alignment](https://mlsanity.com/paper/2007.07066/) (2020)
- [Supervised mid-level features for word image representation](https://mlsanity.com/paper/1410.5224/) (2014)

## Abstract (arXiv)

Scene text recognition has made significant progress in recent years and has become an important part of the work-flow. The widespread use of mobile devices opens up wide possibilities for using OCR technologies in everyday life. However, lack of training data for new research in this area remains relevant. In this article, we present a new dataset consisting of real shots on smartphones and demonstrate the effectiveness of profile normalization in this task. In addition, the influence of various augmentations during the training of models for analyzing document images on smartphones is studied in detail. Our dataset is publicly available.