ATLAS: Adaptive Text Localization Algorithm in High Color Similarity Background
Abstract: One of the major
problems that occur in text localization process is the issue of color
similarity between text and background image. The limitation of localization
algorithms due to high color similarity is highlighted in several research
papers. Hence, this research focuses towards the improvement of text localizing
capability in high color background image similarity by introducing an adaptive
text localization algorithm (ATLAS). ATLAS is an edge-based text localization
algorithm that consists of two parts. TextBackground Similarity Index (TBSI)
being the first part of ATLAS, measures the similarity index of every text
region while the second, Multi Adaptive Threshold (MAT), performs multiple
adaptive thresholdscalculation using size filtration and degree deviation for
locating the possible text region. In this research,ATLAS is verified and
compared with other localization techniques based on two parameters, localizingstrength
and precision. The experiment has been implemented and verified using two types
of datasets, generated text color spectrum dataset and Document Analysis and
Recognition dataset (ICDAR). The result shows ATLAS has significant improvement
on localizing strength and slight improvement on precision compared with other
localization algorithms in high color text-background image.
Author: Lih Fong Wong, Mohd.
Yazid Idris
Journal Code: jptkomputergg150102
