Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Optical Font Recognition Using Typographical Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Font Recognition Based on Global Texture Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
Font Recognition and Contextual Processing for More Accurate Text Recognition
ICDAR '97 Proceedings of the 4th International Conference on Document Analysis and Recognition
Textons, Contours and Regions: Cue Integration in Image Segmentation
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
High-order statistical texture analysis--font recognition applied
Pattern Recognition Letters
Pattern Recognition Letters
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A robust Font Recognition (OFR) is proposed in this work; this is based on the analysis of texture characteristics of document text using invariant moments (invariant to scale, rotation and translation). There is not need of explicit local analysis in our method since the central moments features are extracted as a global characteristic from each font. A printed text block with a unique font is suitable to provide the specific texture properties necessary for the process of recognition. The used fonts were: Courier, Arial, Bookman Old Style, Franklin Gothic Medium, Comic Sans, Impact, Modern and Times New Roman; and their respective styles: regular, italic, bold, italic with bold. The invariant moment technique is used in this study to extract the font characteristics by window size estimation; from an entry text set a data base was build for the learning stage, and then standard statistical classifiers were applied for the identification stage (combining Gaussian and KNN classifiers). We found that the invariant moments change significantly when the textures are rotated and scaled as digital images; good recognition rate was obtained in font recognition with noise.