Paper Info
Title
On the recognition of printed characters of any font and size.
Abstract
We describe the current state of a system that recognizes printed text of various fonts and sizes for the Roman alphabet. The system combines several techniques in order to improve the overall recognition rate. Thinning and shape extraction are performed directly on a graph of the run-length encoding of a binary image. The resulting strokes and other shapes are mapped, using a shape-clustering approach, into binary features which are then fed into a statistical Bayesian classifier. Large-scale trials have shown better than 97 percent top choice correct performance on mixtures of six dissimilar fonts, and over 99 percent on most single fonts, over a range of point sizes. Certain remaining confusion classes are disambiguated through contour analysis, and characters suspected of being merged are broken and reclassified. Finally, layout and linguistic context are applied. The results are illustrated by sample pages.
Year
DOI
Venue
1987
10.1109/TPAMI.1987.4767901
IEEE Trans. Pattern Anal. Mach. Intell.
Keywords
Field
DocType
contour analysis,percent top choice,binary feature,dissimilar font,large-scale trial,correct performance,printed characters,current state,certain remaining confusion class,roman alphabet,binary image,layout,bayesian classifier,shape,manufacturing,feature extraction,pediatrics,bayesian methods,run length encoding,classification algorithms,computer graphics,data mining
Computer vision,Pattern recognition,Naive Bayes classifier,Computer science,Binary image,Font,Feature extraction,Artificial intelligence,Statistical classification,Classifier (linguistics),Computer graphics,Binary number
Journal
Volume
Issue
ISSN
9
2
0162-8828
Citations 
PageRank 
References 
128
196.17
0
Authors
3
Search Limit
100128
Name
Order
Citations
PageRank
Simon Kahan1279209.26
Theo Pavlidis224591848.41
H. S. Baird3214479.51