H-DIBCO 2010 - Handwritten Document Image Binarization Competition

H-DIBCO 2010 is the International Document Image Binarization Contest which is dedicated to handwritten document images organized in conjunction with ICFHR 2010 conference. The general objective of the contest is to identify current advances in handwritten document image binarization using meaningful evaluation performance measures. This paper reports on the contest details including the evaluation measures used as well as the performance of the 17 submitted methods along with a short description of each method.

[1]  Ioannis Pratikakis,et al.  ICDAR 2009 Document Image Binarization Contest (DIBCO 2009) , 2009, 2009 10th International Conference on Document Analysis and Recognition.

[2]  Mohamed Cheriet,et al.  RSLDI: Restoration of single-sided low-quality document images , 2009, Pattern Recognit..

[3]  Matti Pietikäinen,et al.  Adaptive document image binarization , 2000, Pattern Recognit..

[4]  B. Kapralos,et al.  I An Introduction to Digital Image Processing , 2022 .

[5]  Mohamed Cheriet,et al.  Application of Multi-Level Classifiers and Clustering for Automatic Word Spotting in Historical Document Images , 2009, 2009 10th International Conference on Document Analysis and Recognition.

[6]  Matthieu Cord,et al.  Text segmentation in natural scenes using Toggle-Mapping , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

[7]  Mohamed Cheriet,et al.  EFDM : Restoration of Single-sided Low-quality Document Images , 2008 .

[8]  Mohamed Cheriet,et al.  A multi-scale framework for adaptive binarization of degraded document images , 2010, Pattern Recognit..

[9]  Christophe Collet,et al.  From hyperconnections to hypercomponent tree: Application to document image binarization , 2010 .

[10]  N. Otsu A threshold selection method from gray level histograms , 1979 .

[11]  Ioannis Pratikakis,et al.  An Objective Evaluation Methodology for Document Image Binarization Techniques , 2008, 2008 The Eighth IAPR International Workshop on Document Analysis Systems.