Multichannel Texture Analysis Using Localized Spatial Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised texture segmentation using Gabor filters
Pattern Recognition
Font and function word identification in document recognition
Computer Vision and Image Understanding
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
A system for machine-written and hand-written character distinction
ICDAR '95 Proceedings of the Third International Conference on Document Analysis and Recognition (Volume 2) - Volume 2
Multifont Classification Using Typographical Attributes
ICDAR '99 Proceedings of the Fifth International Conference on Document Analysis and Recognition
High-order statistical texture analysis--font recognition applied
Pattern Recognition Letters
Dimensionality reduction using genetic algorithms
IEEE Transactions on Evolutionary Computation
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Font recognition is a fundamental issue in the identification, analysis and reconstruction of documents. In this paper, a new method of optical font recognition is proposed which could recognize the font of every Chinese character. It employs a statistical method based on global texture analysis to recognize a predominant font, and uses a traditional recognizer of a single font to identify the font of a single character by the guidance of an obtained predominant font. It consists of three steps. First, the guiding fonts are acquired based on Gabor features. Then a font recognizer is run to identify the font of the characters one by one. Finally, a post-processing is fulfilled according to the layout knowledge to correct the errors of font recognition. Experiments are carried out and the results show that this method is of immense practical and theoretical value.