Texture-Based Approach for Text Detection in Images Using Support Vector Machines and Continuously Adaptive Mean Shift Algorithm

By Kwang In Kim, Keechul Jung, Jin Hyung Kim. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003

523 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/14bbcdc1744cc5982ffb64ea4755a72921d98d08

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

Citations in the corpus

A Hybrid Approach to Detect and Localize Texts in Natural Scene Images (2011, methodology): “To capture texts of variable scales, Kim et al. [14] extract gray-level texture features from all local regions in each layer of image pyramid.”
Robust Scene Text Detection with Convolution Neural Network Induced MSER Trees (2014, methodology): “The sliding-window based methods detect text information by sliding a sub-window in multiple scales through all locations of an image [11,3,9,28,29,18,1].”
Text-Attentional Convolutional Neural Network for Scene Text Detection (2015, methodology): “The sliding-window methods detect text information by moving a multiscale sub-window through all possible locations in an image [2], [4], [8], [12]–[17].”
Detecting texts of arbitrary orientations in natural images (2012, methodology): “There have been a large number of methods dealing with text detection in natural images and videos [18, 16, 30, 27, 22, 26].”
A Unified Framework for Multioriented Text Detection and Recognition (2014, methodology): “Previous methods either only tackle one of these two tasks [5], [9], [13], [18], [20] or treat them as two isolated stages in an end-to-end text recognition system [6], [17], [21].”
Fast and robust text detection in images and video frames (2005, methodology): “[13] propose a texture-based method using support vector machine (SVM).”
Deep Matching Prior Network: Toward Tighter Multi-oriented Text Detection (2017, methodology): “Another mainstream method is based on sliding window [2, 15, 8, 17], which shifts a window in each position with multiple scales from an image to detect text.”
FOTS: Fast Oriented Text Spotting with a Unified Network (2018, methodology): “Sliding-window-based methods [22, 28, 3, 54] and connected-components based methods [18, 40, 2] are two representative categories in conventional methods.”
Text Detection and Recognition in Imagery: A Survey (2015, methodology): “[19] proposed using SVMs and texture templates to perform text localization.”
A Fast Uyghur Text Detector for Complex Background Images (2018, background): “Hence, an extra text/non-text classifier is required, such as CNN [35], SVM [8], and random forest [2] etc. Yin et al. presented a MSER-tree pruning algorithm to remove false detections [5].”
Classifying materials in the real world (2010, methodology): “This method was then applied to text detection in images, combined with a continuously adaptive mean shift algorithm [40].”
Scene text detection and recognition: recent advances and future trends (2015, methodology): “2 Text detection examples of the algorithm of Kim et al [36].”
Arbitrary-Oriented Scene Text Detection via Rotation Proposals (2017, methodology): “Sliding window-based methods [7], [10], [28]–[30] tend to use a sliding window of a fixed size to slide the text area and find the region most likely to include text.”
Simplifying Mixture Models Through Function Approximation (2006, background): “mentation [5], [6], tracking [7], [20], [45], and video processing [10], [26].”