NDLI: Tool for automatic tuning of binarisation techniques

Content Provider	IET Digital Library
Author	Kefali, Abderrahmane Sari, Toufik
Abstract	Most of the proposed binarisation methods include parameters that must be set correctly before use. The determination of the values of these parameters is made most of the time manually after several tests. However, the optimum parameter values differ from an image to another and therefore the parameterisation shall be carried out for each image separately. In fact, as this task is very difficult, even impossible for large collections of images, the tuning is usually done once for the entire image collection. In this study, the authors propose a tool for automatic and adaptive parameterisation of binarisation techniques for each image separately. The adopted methodology is based on the use of an artificial neural network (ANN) to learn the optimal parameter values of a binarisation method for a set of images (training set), based on their features, and to use the trained ANN to determine the optimal parameter values for other images not learned. Several experiments have been conducted on images of degraded documents and the obtained results are encouraging.
Starting Page	2192
Ending Page	2203
Page Count	12
ISSN	17519659
Volume Number	12
e-ISSN	17519667
Issue Number	Issue 12, Dec (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/12/12
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.5132
Journal	IET Image Processing
Publisher Date	2018-08-21
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Adaptive Parameterisation Artificial Neural Network Automatic Parameterisation Automatic Tuning Binarisation Technique Computer Vision And Image Processing Technique Document Image Processing Document Processing Technique Image Collection Image Segmentation Learning in AI Neural Computing Technique Neural Nets Optical, Image And Video Signal Processing Optimal Parameter Value Optimum Parameter Value Trained ANN Training Set
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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