Text localization in natural scene images by mean-shift clustering and parallel edge feature

Authors:
Huy Phat Le;Nguyen Dinh Toan;SangCheol Park;GueeSang Lee
Affiliations:
Chonnam National University, Gwangju, Korea;Chonnam National University, Gwangju, Korea;Chonnam National University, Gwangju, Korea;Chonnam National University, Gwangju, Korea
Venue:
Proceedings of the 5th International Conference on Ubiquitous Information Management and Communication
Year:
2011

Citing 4
Cited 1

Mean Shift: A Robust Approach Toward Feature Space Analysis

IEEE Transactions on Pattern Analysis and Machine Intelligence
Locating Characters in Scene Images Using Frequency Features

ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Text Detection and Localization in Complex Scene Images using Constrained AdaBoost Algorithm

ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
Fast and robust text detection in images and video frames

Image and Vision Computing

Text extraction from natural scene image: A survey

Neurocomputing

Quantified Score

Hi-index	0.00

Visualization

Abstract

A new text localization method using the parallel edge feature of text strokes is proposed, based on the observation that text-stroke consists of two edges in parallel. First, mean-shift clustering is employed to group similar pixels into clusters. The connected components in each cluster are considered as candidates for text strokes. Then, parallel edges are detected to verify whether the connected components are text strokes. The contribution of this paper is the presentation of a new feature of parallel edges along the stroke, providing structural information for the text localization. The performance, evaluated on ICDAR2003 image database, shows that the proposed algorithm works successfully with most of the text images.