Automated Document Conversion System for Simple Multimedia Platforms

Martinez-Alvarez, R. P.; Costas-Rodriguez, S.; González-Castaño, Francisco J.; Gil‐Castiñeira, Felipe

doi:10.1109/ccnc.2010.5421807

Cited by 3 publications

(2 citation statements)

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“…Several researchers have focused on text knowledge extraction (data mining) of PDF and image documents ( [10], [13]). This field is termed as text mining and many organizations internationally have already realized the potentiality of text mining.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Classification Algorithms to Recognize Chart Types in Portable Document Format (PDF) Files

Karthikeyani¹,

Nagarajan²

2012

IJCA

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Chart recognition system from PDF files is a relatively young research field where techniques and algorithms are proposed to identify type of charts and interpret them. This paper focus on recognition of chart type that is a part of PDF document using texture features and classification algorithm. Eleven types of texture features and three classifiers, namely, Multilayer perceptron, support vector machine and K nearest neighbour, are used. Performance analysis of the proposed chart type recognition systems show that texture features for chart type recognition has promising future and produces best result while using KNN and SVM algorithm.

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Section: Introductionmentioning

confidence: 99%

Machine Learning Classification Algorithms to Recognize Chart Types in Portable Document Format (PDF) Files

Karthikeyani¹,

Nagarajan²

2012

IJCA

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“…For this purpose, the existing PHC can be categorized into two domains, namely, text recognition and graphics recognition. Text recognition focus on the textual part of the PDF files and uses Optical Character Recognition (OCR) algorithms during conversion (Islam et al, 2009;Martinez-Alvarez et al, 2010). Graphics recognition, on the other hand, is focused on the lines and symbols of the PDF file.…”

Section: Introductionmentioning

confidence: 99%

Ensemble Classification System for Scientific Chart Recognition from PDF Files

Nagarajan¹,

Karthikeyani

2012

International Journal of Computer Vision and Image Processing

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Portable Document Format (PDF) is the most frequently used universal document format on the Internet and E-Publishing. Wide usage of PDF files has increased the need of conversion tools that convert PDF file content to text or HTML formats. A PDF converter can be categorized into two domains, namely, text recognition and graphics recognition. This paper focus on graphic recognition, especially chart type identification, which is concerned with developing algorithms that has the ability to determine the type of a given chart image from a PDF file. In the proposed system, initially an enhanced connected component and statistical feature based method is used to separate the chart region from other regions. The chart region is then analyzed and grouped as either 2-dimensional or 3-dimensional chart. After separating the graphic component from the text components, feature extraction is performed. The features can be grouped as object features, texture features and shape features. The combined feature vector is then classified using ensemble classification system. Experimental results show that the chart separation, feature extraction and ensemble classification models significantly improve the quality of chart identification.

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Scientific Chart image property identification by connected component labeling in PDF files

Karthikeyani¹,

Nagarajan²

2011

2011 3rd International Conference on Electronics Computer Technology

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Automated Document Conversion System for Simple Multimedia Platforms

Cited by 3 publications

References 0 publications

Machine Learning Classification Algorithms to Recognize Chart Types in Portable Document Format (PDF) Files

Machine Learning Classification Algorithms to Recognize Chart Types in Portable Document Format (PDF) Files

Ensemble Classification System for Scientific Chart Recognition from PDF Files

Scientific Chart image property identification by connected component labeling in PDF files

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