Journal
EXPERT SYSTEMS WITH APPLICATIONS
Volume 39, Issue 4, Pages 4032-4043Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2011.09.162
Keywords
Optical character recognition; Text segmentation; Binarization; Learning
Categories
Funding
- Spanish Ministerio de Ciencia e Innovacion [TIN2010-20845-C03-01]
Ask authors/readers for more resources
In this paper text segmentation in generic displays is proposed through learning the best binarization values for a commercial optical character recognition (OCR) system. The commercial OCR is briefly introduced as well as the parameters that affect the binarization for improving the classification scores. The purpose of this work is to provide the capability to automatically evaluate standard textual display information, so that tasks that involve visual user verification can be performed without human intervention. The problem to be solved is to recognize text characters that appear on the display, as well as the color of the characters' foreground and background. The paper introduces how the thresholds are learnt through: (a) selecting lightness or hue component of a color input cell, (b) enhancing the bitmaps' quality, and (c) calculating the segmentation threshold range for this cell. Then, starting from the threshold ranges learnt at each display cell, the best threshold for each cell is gotten. The input and output data sets for testing the algorithms proposed are described, as well as the analysis of the results obtained. (C) 2011 Elsevier Ltd. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available