An EMD-based recognition method for Chinese fonts and styles

Yang, Zhihua; Yang, Lihua; Qi, Dongxu; Suen, Ching Y.

doi:10.1016/j.patrec.2006.03.002

Cited by 40 publications

(19 citation statements)

References 11 publications

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“…A similar approach can be found in an earlier paper by Miao et al [12]. In Yang et al [23], Chinese fonts are recognized based on Empirical Mode Decomposition, or EMD (should not be confused with the distance metric with the same abbreviation). The proposed method is based on the definition of five basic strokes that are common in Chinese characters.…”

Section: Related Workmentioning

confidence: 96%

A Font Search Engine for Large Font Databases

Solli

Lenz

2011

ELCVIA

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A search engine for font recognition is presented and evaluated. The intended usage is the search in very large font databases. The input to the search engine is an image of a text line, and the output is the name of the font used when rendering the text. After pre-processing and segmentation of the input image, a local approach is used, where features are calculated for individual characters. The method is based on eigenimages calculated from edge filtered character images, which enables compact feature vectors that can be computed rapidly. In this study the database contains 2763 different fonts for the English alphabet. To resemble a real life situation, the proposed method is evaluated with printed and scanned text lines and character images. Our evaluation shows that for 99 % of the queries, the correct font name can be found within the five best matches.

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Section: Related Workmentioning

confidence: 96%

A Font Search Engine for Large Font Databases

Solli

Lenz

2011

ELCVIA

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“…In the literature, only some researchers tested their approaches against noisy document images [6,8,12]. To generate noisy text images, they artificially added noise with different SNR (signal to noise ratio) values to the test document images.…”

Section: Noise Robustness Analysismentioning

confidence: 99%

“…Applying an OCR to a machine-printed text with known font causes more accurate result than when the font of the input text is unknown [1]. Several works have been done in optical font recognition in various languages such as Latin [2][3][4][5][6][7][8][9][10], Chinese [6,[11][12][13], Arabic [14][15][16], and Farsi [17][18][19][20]. In all of these works, the font of the whole text in a document image was assumed to be uniform.…”

Section: Introductionmentioning

confidence: 99%

Farsi Font Recognition Based On The Fonts Of Text Samples Extracted By Som

Ziaratban¹,

Bagheri²

2015

J. Math. Computer Sci.

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A Farsi font recognition algorithm based on the fonts of some frequent text samples is proposed. Some features are extracted from the connected components of a text image. The feature vectors are clustered by using a Self-Organizing Map (SOM) clustering method. The clusters with more members determine the most frequent connected components (MFCCs). A number of members of these big clusters are extracted from the input image. This procedure is applied to both training and test images. Since the frequent samples in different Farsi texts are very similar, it can be guaranteed that a large number of samples of the detected MFCCs for a test image surely are in the extracted training samples set. The font type and font style of the extracted test samples are recognized by matching between them and the training samples. The most frequent recognized font of the extracted samples is considered as the font of the input text. To achieve a more accurate algorithm with lower complexity, the font size is determined in the second phase after the phase of the font type and style recognition. Using a lexicon reduction procedure reduces the complexities and processing time. The font size estimation is carried out based on the size of a particular MFCC in a text image. Experiments show that the proposed method outperforms other font recognition methods.

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“…Huang et al 12 presented a multiresolution decomposition technique, referred to as EMD, which was originally proposed for the study of ocean waves, 12 and was later found potentially applicable to geophysical exploration, underwater acoustic signals, noise removal filter, biomedicine, pattern recognition, etc. [13][14][15][16][17] . The major advantage of using EMD is that the basis functions can be directly derived from the signal itself based on the local characteristic time scale of the data, which provides better characterization than those given in advance.…”

Section: Feature Face Extraction 21 Emdmentioning

confidence: 99%

Empirical mode decomposition-based facial pose estimation inside video sequences

2010

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Abstract. We describe a new pose-estimation algorithm via integration of the strength in both empirical mode decomposition ͑EMD͒ and mutual information. While mutual information is exploited to measure the similarity between facial images to estimate poses, EMD is exploited to decompose input facial images into a number of intrinsic mode function ͑IMF͒ components, which redistribute the effect of noise, expression changes, and illumination variations as such that, when the input facial image is described by the selected IMF components, all the negative effects can be minimized. Extensive experiments were carried out in comparisons to existing representative techniques, and the results show that the proposed algorithm achieves better pose-estimation performances with robustness to noise corruption, illumination variation, and facial expressions.

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An EMD-based recognition method for Chinese fonts and styles

Cited by 40 publications

References 11 publications

A Font Search Engine for Large Font Databases

A Font Search Engine for Large Font Databases

Farsi Font Recognition Based On The Fonts Of Text Samples Extracted By Som

Empirical mode decomposition-based facial pose estimation inside video sequences

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