A Text Location Method for Web Images

2014 ◽  
Vol 926-930 ◽  
pp. 3350-3353
Author(s):  
Xin Ning ◽  
Wei Jun Li ◽  
Wen Jie Liu
Keyword(s):  
Recall Rate ◽  
Cascade Classifier ◽  
Edge Image ◽  
Text Localization ◽  
Localization Method ◽  
Location Method ◽  
Candidate Image ◽  
Strong Robustness ◽  
Web Images ◽  
Text Location

This paper proposes a text localization method with multi-features based on cascade classifier for a variety of web images. Specifically, first, the original image is divided into sub-images with different scales, which form more satisfactory edge image blocks after being pretreated respectively; then, the researchers determine in the classifier whether the text area is contained in the candidate image blocks according to the edge connectivity characteristics, stroke density characteristics and text arrangement characteristics of text area; finally, the location results of sub-images with different scales are mixed together to obtain the final result. The experiments show that this location method has the relatively high precision and recall rate and quite strong robustness, which is suitable for a variety of web images.

AUTOMATIC DETECTION AND VERIFICATION OF TEXT REGIONS IN NEWS VIDEO FRAMES

2002 ◽  
Vol 16 (02) ◽  
pp. 257-271 ◽  
Author(s):  
JIANMING HU ◽  
JIE XI ◽  
LIDE WU
Keyword(s):  
Detection Algorithm ◽  
Difficult Problem ◽  
Edge Image ◽  
Textual Information ◽  
Run Length ◽  
Video Frames ◽  
News Video ◽  
Length Analysis ◽  
Text Location ◽  
Automatic Text

Textual information in a video is very useful for video indexing and retrieving. Detecting text blocks in video frames is the first important procedure for extracting the textual information. Automatic text location is a very difficult problem due to the large variety of character styles and the complex backgrounds. In this paper, we describe the various steps of the proposed text detection algorithm. First, the gray scale edges are detected and smoothed horizontally. Second, the edge image is binarized, and run length analysis is applied to find candidate text blocks. Finally, each detected block is verified by an improved logical level technique (ILLT). Experiments show this method is not sensitive to color/texture changes of the characters, and can be used to detect text lines in news videos effectively.

TRAVELOGUE ENRICHING AND SCENIC SPOT OVERVIEW BASED ON TEXTUAL AND VISUAL TOPIC MODELS

2011 ◽  
Vol 25 (03) ◽  
pp. 373-390 ◽  
Author(s):  
YANWEI PANG ◽  
XIN LU ◽  
YUAN YUAN ◽  
XUELONG LI
Keyword(s):  
Visual Information ◽  
Probabilistic Models ◽  
Layer Model ◽  
Comprehensive Overview ◽  
Image Layer ◽  
Scenic Spot ◽  
Candidate Image ◽  
Image Set ◽  
Web Images ◽  
Historical Sites

We consider the problem of enriching the travelogue associated with a small number (even one) of images with more web images. Images associated with the travelogue always consist of the content and the style of textual information. Relying on this assumption, in this paper, we present a framework of travelogue enriching, exploiting both textual and visual information generated by different users. The framework aims to select the most relevant images from automatically collected candidate image set to enrich the given travelogue, and form a comprehensive overview of the scenic spot. To do these, we propose to build two-layer probabilistic models, i.e. a text-layer model and image-layer models, on offline collected travelogues and images. Each topic (e.g. Sea, Mountain, Historical Sites) in the text-layer model is followed by an image-layer model with sub-topics learnt (e.g. the topic of sea is with the sub-topic like beach, tree, sunrise and sunset). Based on the model, we develop strategies to enrich travelogues in the following steps: (1) remove noisy names of scenic spots from travelogues; (2) generate queries to automatically gather candidate image set; (3) select images to enrich the travelogue; and (4) choose images to portray the visual content of a scenic spot. Experimental results on Chinese travelogues demonstrate the potential of the proposed approach on tasks of travelogue enrichment and the corresponding scenic spot illustration.

A Low Complexity Sign Detection and Text Localization Method for Mobile Applications

2011 ◽  
Vol 13 (5) ◽  
pp. 922-934 ◽  
Author(s):  
Katherine L. Bouman ◽  
Golnaz Abdollahian ◽  
Mireille Boutin ◽  
Edward J. Delp
Keyword(s):  
Mobile Applications ◽  
Low Complexity ◽  
Text Localization ◽  
Localization Method ◽  
Sign Detection

scholarly journals Edge Location Method for Multidimensional Image Based on Edge Symmetry Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Chen Li
Keyword(s):  
Vertical Direction ◽  
Region Of Interest ◽  
Basic Feature ◽  
Vertical Gradient ◽  
Edge Region ◽  
Image Region ◽  
Location Method ◽  
Candidate Image ◽  
Positioning Method ◽  
Edge Location

The most basic feature of an image is edge, which is the junction of one attribute area and another attribute area in the image. It is the most uncertain place in the image and the place where the image information is most concentrated. The edge of an image contains rich information. So, the edge location plays an important role in image processing, and its positioning method directly affects the image effect. In order to further improve the accuracy of edge location for multidimensional image, an edge location method for multidimensional image based on edge symmetry is proposed. The method first detects and counts the edges of multidimensional image, sets the region of interest, preprocesses the image with the Gauss filter, detects the vertical edges of the filtered image, and superposes the vertical gradient values of each pixel in the vertical direction to obtain candidate image regions. The symmetry axis position of the candidate image region is analyzed, and its symmetry intensity is measured. Then, the symmetry of vertical gradient projection in the candidate image region is analyzed to verify whether the candidate region is a real edge region. The multidimensional pulse coupled neural network (PCNN) model is used to synthesize the real edge region after edge symmetry processing, and the result of edge location of the multidimensional image is obtained. The results show that the method has strong antinoise ability, clear edge contour, and precise location.

Sign in / Sign up

Export Citation Format

Share Document