scholarly journals Processing Dangerous Paths – On Security and Privacy of the Portable Document Format

2021 ◽  
Author(s):  
Jens Müller ◽  
Dominik Noss ◽  
Christian Mainka ◽  
Vladislav Mladenov ◽  
Jörg Schwenk
Keyword(s):  
Portable Document Format ◽  
Security And Privacy ◽  
Document Format

Security and privacy issues in the Portable Document Format

2010 ◽  
Vol 83 (10) ◽  
pp. 1813-1822 ◽  
Author(s):  
Aniello Castiglione ◽  
Alfredo De Santis ◽  
Claudio Soriente
Keyword(s):  
Portable Document Format ◽  
Security And Privacy ◽  
Privacy Issues ◽  
Document Format

scholarly journals PDF: The "P" Stands for Problematic

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Julia Caffrey-Hill ◽  
Nathan Clark ◽  
Brent Davis ◽  
William Helman
Keyword(s):  
Higher Education ◽  
Visually Impaired ◽  
Portable Document Format ◽  
Limited Budget ◽  
File Formats ◽  
Level Of Effort ◽  
Document Format

The Portable Document Format (PDF) is one of the most common document file types in academia, both in the library and the classroom. Unfortunately, PDF poses unique barriers to accessibility, particularly for the visually impaired. Ensuring that all people can read PDF content can be complex and expensive. There are alternative formats that can be made accessible with a lower level of effort, providing a better experience for both the end reader and the document author. This article serves as a call to arms for higher education to migrate away from PDF and to urge the tech community to develop new file formats that lend themselves to enhanced accessibility on a limited budget.

Ensemble Classification System for Scientific Chart Recognition from PDF Files

2012 ◽  
Vol 2 (4) ◽  
pp. 1-10 ◽  
Author(s):  
S. Nagarajan ◽  
V. Karthikeyani
Keyword(s):  
Feature Extraction ◽  
Classification System ◽  
Texture Features ◽  
Portable Document Format ◽  
Ensemble Classification ◽  
3 Dimensional ◽  
Feature Based ◽  
Separation Feature ◽  
Object Features ◽  
Document Format

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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