Localizing and segmenting text in images and videos

By Rainer Lienhart, Axel Wernicke. IEEE Trans. Circuits Syst. Video Technol., 2002

482 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/8387d4998f810cd2b60bd81545cb993087bc8788

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. Cong Yao
3. Andrew Zisserman
4. Jiri Matas
5. Lukás Neumann
6. Qixiang Ye
7. Andrea Vedaldi
8. Luc Van Gool

Citations in the corpus

• Text Localization in Real-World Images Using Efficiently Pruned Exhaustive Search (2011, background): “Methods in the first category [2], [3] use a window which is moved over the image and the presence of text is estimated on the basis of local image features.”
• Detecting text in natural scenes with stroke width transform (2010, background): “The problem of text detection was considered in a number of recent studies [1, 2, 3, 4, 5, 6, 7].”
• Text Detection and Recognition in Imagery: A Survey (2015, methodology): “Despite the effectiveness of multi-frame integration for static video captions [14], [69], [103], there are challenges when addressing moving video captions and video scene”
• Comparative study on methods of detecting research fronts using different types of citation (2009, methodology): “Next, the modified coarse-to-fine, topdown block segmentation algorithm ( Lienhart & Wernicke, 2002 ) was used to precisely locate the text components.”
• A Hybrid Approach to Detect and Localize Texts in Natural Scene Images (2011, methodology): “To make use of color information for text localization in videos, the method of Lienhar and Wernicke [20] computes the gradient map with color derivative operators.”
• A framework for improved video text detection and recognition (2012, background): “[20] proposed an approach for text detection in both videos and images.”
• Text detection and recognition in images and video frames (2004, methodology): “As an alternative, a few systems considered machine learning tools to perform the text detection [10] [15].”
• Extracting code from programming tutorial videos (2016, background): “Although there has been much research on automatic indexing of videos, and on extracting text from videos [8], most of it has focused on detecting small amounts of text (e.”
• Real-time scene text localization and recognition (2012, methodology): “Methods based on a sliding window [6, 2, 7] limit the search to a subset of image rectangles.”
• Fast and robust text detection in images and video frames (2005, methodology): “Although the recall rate is a little bit lower than that of [6], the false alarm rate (2.”
• ICDAR 2003 robust reading competitions (2003, background): “In recent years there has been some significant research into these general reading systems that are able to locate and/or read text in scene images [11, 2, 3, 4, 1, 10].”