Empirical Performance Evaluation of Raster-to-Vector Conversion Methods: A Study on Multi-Level Interactions between Different Factors

Hasan S.M. AL-KHAFFAF  Abdullah Z. TALIB  Rosalina ABDUL SALAM  

IEICE TRANSACTIONS on Information and Systems   Vol.E94-D   No.6   pp.1278-1288
Publication Date: 2011/06/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.E94.D.1278
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Image Recognition, Computer Vision
raster-to-vector conversion,  performance evaluation,  salt/pepper noise,  engineering drawing,  binary image,  statistical analysis,  repeated measure ANOVA,  document analysis and recognition,  

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Many factors, such as noise level in the original image and the noise-removal methods that clean the image prior to performing a vectorization, may play an important role in affecting the line detection of raster-to-vector conversion methods. In this paper, we propose an empirical performance evaluation methodology that is coupled with a robust statistical analysis method to study many factors that may affect the quality of line detection. Three factors are studied: noise level, noise-removal method, and the raster-to-vector conversion method. Eleven mechanical engineering drawings, three salt-and-pepper noise levels, six noise-removal methods, and three commercial vectorization methods were used in the experiment. The Vector Recovery Index (VRI) of the detected vectors was the criterion used for the quality of line detection. A repeated measure ANOVA analyzed the VRI scores. The statistical analysis shows that all the studied factors affected the quality of line detection. It also shows that two-way interactions between the studied factors affected line detection.