The nature of statistical learning theory
The nature of statistical learning theory
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 novel pitch period detection algorithm based on hilbert-huang transform
SINOBIOMETRICS'04 Proceedings of the 5th Chinese conference on Advances in Biometric Person Authentication
A method to eliminate riding waves appearing in the empirical AM/FM demodulation
Digital Signal Processing
Farsi font recognition based on Sobel-Roberts features
Pattern Recognition Letters
New features using fractal multi-dimensions for generalized Arabic font recognition
Pattern Recognition Letters
An oblique-extrema-based approach for empirical mode decomposition
Digital Signal Processing
Intrinsic nonlinear multiscale image decomposition: A 2D empirical mode decomposition-like tool
Computer Vision and Image Understanding
Iterative Filtering Decomposition Based on Local Spectral Evolution Kernel
Journal of Scientific Computing
Pattern Recognition Letters
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This paper presents a novel method to recognize Chinese fonts based on empirical mode decomposition (EMD). By analyzing and comparing a great number of Chinese characters, five basic strokes have been selected to characterize the stroke features of Chinese fonts. Based on them, stroke feature sequences of a given text block are calculated. By decomposing them with EMD, some intrinsic mode functions are produced and then the first two, which are of the highest frequencies, are used to produce the so-called stroke high frequency energies, which is the average energy of the two intrinsic mode functions over the length of the sequence. By calculating the stroke high frequency energies for all the five basic strokes and combining them with the averages of the five residues, which are called stroke low frequency energies, a 10-dimensional feature vector is formed. Finally, the minimum distance classifier is used to recognize the fonts and encouraging experimental results have been obtained. The main advantages of our algorithm are that (1) the feature dimension is very low; (2) less samples are needed to train the classifier; (3) finally and most importantly, it is the first attempt to apply the new theory of Hilbert-Huang transform to document analysis and recognition.