Text information extraction in images and video: a survey

By Keechul Jung, Kwang In Kim, Anil K. Jain. 2004

315 citations recorded by the source corpus.

Text data present in images and video contain useful information for automatic annotation, indexing, and structuring of images. Extraction of this information involves detection, localization, tracking, extraction, enhancement, and recognition of the text from a given image. However, variations of text due to differences in size, style, orientation, and alignment, as well as low image contrast and complex background make the problem of automatic text extraction extremely challenging. While comprehensive surveys of related problems such as face detection, document analysis, and image & video indexing can be found, the problem of text information extraction is not well surveyed. A large number of techniques have been proposed to address this problem, and the purpose of this paper is to classify and review these algorithms, discuss benchmark data and performance evaluation, and to point out promising directions for future research.

Subfields: Computer Vision and Pattern Recognition

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Xiang Bai
2. Cong Yao
3. Xin He
4. Qixiang Ye
5. Shangbang Long
6. Yingli Tian
7. Yu Qiao
8. Luc Van Gool

Citations in the corpus

• Framework for Performance Evaluation of Face, Text, and Vehicle Detection and Tracking in Video: Data, Metrics, and Protocol (2009, unknown): “There are several published efforts addressing the problem of text area detection in video [48].”
• Text Detection and Recognition in Imagery: A Survey (2015, background): “tion procedures, and text information extraction [32], [85], which focuses on both localization and binarization.”
• Scene Text Detection and Recognition: The Deep Learning Era (2018, methodology): “For scene text detection and recognition in videos, please also refer to Jung et al. (2004) and Yin et al.”
• A Unified Framework for Multioriented Text Detection and Recognition (2014, background): “In conclusion, there is still room for improvement for both text detection and recognition in natural scenes, as related real-world applications have high requirements for reliable text information extraction [44].”
• text Detection with Convolutional Neural Networks (2008, background): “A survey on text detection can be found in (Jung et al., 2004).”
• Arbitrary-Oriented Scene Text Detection via Rotation Proposals (2017, background): “Reading text in the wild has been studied during the last few decades and the comprehensive surveys can be found at [2, 16, 33, 40].”
• Text String Detection from Natural Scenes by Structure-based Partition and Grouping (2011, methodology): “This is compatible with the detection and localization procedure described in the survey of text extraction algorithms [11], [38].”