Text detection and recognition in images and video frames

By Datong Chen, Jean-Marc Odobez, Hervé Bourlard. 2004

317 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/42a8ff86566538103c6116f9047a4c3128e1542c

How researchers see this paper

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. Luc Van Gool
3. Andrew Zisserman
4. Cong Yao
5. Andrea Vedaldi
6. Yu Qiao
7. Dacheng Tao
8. Chunhua Shen

Citations in the corpus

• Text Detection and Recognition in Imagery: A Survey (2015, methodology): “In [27], edge, gradient and texture features are combined and trained with a multilayer percep-”
• A Unified Framework for Multioriented Text Detection and Recognition (2014, methodology): “Driven by the wide range of potential applications, scene text detection and recognition have been important research topics for decades [7], [8], [35]–[41] and have recently seen a surge in research efforts in the computer vision community [9]–[14], [17], [18], [20], [21], [26], [42].”
• Image Retrieval Using Textual Cues (2013, background): “The problem of recognizing text in images or videos has gained a huge attention in the computer vision community in recent years [5,9,13,18,19,24,25].”
• A framework for improved video text detection and recognition (2012, background): “[4] presented two-stage text detection methods that combine text localization with subsequent verification.”
• A Hybrid Approach to Detect and Localize Texts in Natural Scene Images (2011, methodology): “The approach proposed by Chen et al. [3] extracts texts in video images by detecting horizontal and vertical edges with a Canny filter and smoothing the edge maps to extract candidate text lines with morphological operators.”
• The AMI meeting corpus (2005, methodology): “We intend to subject all of these data sources to further processing in order to extract a more meaningful, character-based data representation automatically [5, 6].”
• ReVision: automated classification, analysis and redesign of chart images (2011, background): “Furthermore, connected components analysis or more sophisticated approaches [4] can extract text region bounding boxes automatically.”
• text Detection with Convolutional Neural Networks (2008, methodology): “Multilayer perceptrons (MLPs), for instance, have been used on top of wavelet features (Li et al., 2000) or Support Vector Machines (SVMs) on top of a carefully selected set of features (Lucas et al., 2005; Chen et al., 2004).”
• The AMI Meeting Corpus: A Pre-announcement (2005, methodology): “We intend to subject all of these data sources to further processing in order to extract a more meaningful, character-based data representation automatically [5, 6].”