Text Detection and Recognition in the Wild: A Review

Abstract

Detection and recognition of text in natural images are two main problems in the field of computer vision that have a wide variety of applications in analysis of sports videos, autonomous driving, industrial automation, to name a few. They face common challenging problems that are factors in how text is represented and affected by several environmental conditions. The current state-of-the-art scene text detection and/or recognition methods have exploited the witnessed advancement in deep learning architectures and reported a superior accuracy on benchmark datasets when tackling multi-resolution and multi-oriented text. However, there are still several remaining challenges affecting text in the wild images that cause existing methods to underperform due to there models are not able to generalize to unseen data and the insufficient labeled data. Thus, unlike previous surveys in this field, the objectives of this survey are as follows: first, offering the reader not only a review on the recent advancement in scene text detection and recognition, but also presenting the results of conducting extensive experiments using a unified evaluation framework that assesses pre-trained models of the selected methods on challenging cases, and applies the same evaluation criteria on these techniques. Second, identifying several existing challenges for detecting or recognizing text in the wild images, namely, in-plane-rotation, multi-oriented and multi-resolution text, perspective distortion, illumination reflection, partial occlusion, complex fonts, and special characters. Finally, the paper also presents insight into the potential research directions in this field to address some of the mentioned challenges that are still encountering scene text detection and recognition techniques.

References in corpus (29)

• Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
• Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
• Deep Residual Learning for Image Recognition
• TextBoxes++: A Single-Shot Oriented Scene Text Detector
• Synthetic Data and Artificial Neural Networks for Natural Scene Text Recognition
• Robust Text Detection in Natural Scene Images
• Focusing Attention: Towards Accurate Text Recognition in Natural Images
• R2CNN: Rotational Region CNN for Orientation Robust Scene Text Detection
• Feature Pyramid Networks for Object Detection
• TextBoxes: A Fast Text Detector with a Single Deep Neural Network
• TextField: Learning A Deep Direction Field for Irregular Scene Text Detection
• Rosetta: Large scale system for text detection and recognition in images
• ICDAR2019 Competition on Scanned Receipt OCR and Information Extraction
• COCO-Text: Dataset and Benchmark for Text Detection and Recognition in Natural Images
• Detecting Curve Text in the Wild: New Dataset and New Solution
• Scene Text Detection via Holistic, Multi-Channel Prediction
• PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection
• Scene Text Detection and Recognition: The Deep Learning Era
• What Is Wrong With Scene Text Recognition Model Comparisons? Dataset and Model Analysis
• Scene Text Recognition with Sliding Convolutional Character Models
• Pyramid Mask Text Detector
• Mask TextSpotter: An End-to-End Trainable Neural Network for Spotting Text with Arbitrary Shapes
• Scene Text Recognition from Two-Dimensional Perspective
• Edit Probability for Scene Text Recognition
• 2D-CTC for Scene Text Recognition
• Omnidirectional Scene Text Detection with Sequential-free Box Discretization
• ICDAR 2019 Competition on Large-scale Street View Text with Partial Labeling -- RRC-LSVT
• Learning to See Through Obstructions
• Image super-resolution reconstruction based on attention mechanism and feature fusion