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In this section, we present the algorithm for distinguishing documents produced by laser printers, inkjet printers, and electrostatic copiers.
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This paper describes a method to distinguish documents produced by laser printers, inkjet printers, and electrostatic copiers, three commonly used document creation devices.
In this paper, we describe a method to distinguish documents produced by laser printers, inkjet printers, and electrostatic copiers, three devices that are commonly used today.
To the best of our knowledge, this is the first work to distinguish documents produced by laser printer, inkjet printer, and copier based on features extracted from individual characters in the documents.
This investigation of the recognition of these novel entity classes was motivated by their potential to distinguish documents associated with T4SS from documents which are not, a common and difficult task faced by annotators working in large-scale bioinformatics resources such as PATRIC.
These two properties are exploited by the two features, the text edge score and the unnaturalness score, that we designed for distinguishing text/mix documents from picture/photo documents.
Two features, luminance variability score and histogram flatness score, are utilized to distinguish text documents from nontext documents.
In his statements and speeches, Trump has often qualified his language by distinguishing between documented and undocumented immigrants.
The proposed approach can distinguish between documents produced by these sources based on the characters in the document.
The proposed approach can distinguish between documents produced by these sources based on features extracted from the characters in the documents.
Retrieved data: Server should not be able to distinguish between documents and determine search contents.
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