PATTERN RECOGNITION IN DIGITAL IMAGES FOR IDENTIFICATION OF SPELLING LINES ADULTERATED BY USING K-MEANS ALGORITHM AND PCA

Autores

  • Hailton David Lemos

Palavras-chave:

handwriting, pca, k-mean, pattern recognition

Resumo

Pattern recognition in digital images for the identification of lines in adulterated
spelling using k-means algorithm and Principal Component Analysis (PCA). Color is
used to classify objects of interest from the analysis and comparison of the numerical
values of the data, pattern recognition and classification information from the
statistical information extracted from patterns through the PCA. The images of the
traits used in the study were obtained on equipment that reflects an ultraviolet
spectrum which focuses on the trait examined and captured by the camera. The
choice of images in the ultraviolet region is made experimentally due to its higher
absorbance capability compared with images obtained in other regions of spectrum.
Two classes of traits: adulterated and unadulterated. The features are those that
receive tainted overlapping somewhere alien. Traces unadulterated do not receive
the addition of any strange spot.

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Publicado

2012-11-30

Como Citar

Lemos, H. D. . (2012). PATTERN RECOGNITION IN DIGITAL IMAGES FOR IDENTIFICATION OF SPELLING LINES ADULTERATED BY USING K-MEANS ALGORITHM AND PCA. ENCICLOPÉDIA BIOSFERA, 8(15). Recuperado de https://conhecer.org.br/ojs/index.php/biosfera/article/view/3808