Sentence examples for distinguish text from inspiring English sources

Two features, luminance variability score and histogram flatness score, are utilized to distinguish text documents from nontext documents.

By multiplying the power of 1.1 by the n, method 3 incorrectly increases the ability of the feature to distinguish text categories.

Zhang (2012, p. 272) claims that "'[c]oherence is the essential condition to distinguish text from non-text", so being coherent is the basic characteristic of a source text which can be understood and translated into another language.

That algorithm works by sequentially applying four simple classifiers to a document: first, a classifier to distinguish color from neutral documents; second, a classifier to distinguish text from non-text documents; another classifier to distinguish mix documents from photos/pictures; and a fourth classifier to decide between photos, pictures, and the mix class, as shown in Fig. 1 a.

Stylometric analysis refers to utilizing domain-specific features (i.e. characteristics) in statistical analyses to compare and distinguish one text document from another.

Viable directions for research include latent variable models, generative adversarial network-inspired models trained to distinguish real text from language model samples, and methods for extracting pseudo-labels from naturally occurring sources.

It not only captures the word frequency in the text but distinguishes the texts.

The program can also distinguish between texts and voice calls; if someone phones you and SMSReplier is activated, they will receive a text message stating that the receiver will call, as opposed to text, later.

Using little more than the zipping programs found on most personal computers, they can easily distinguish between texts written in 10 different languages and almost unfailingly tell which of a large group of texts were written by the same author.

Two subsets of differing quality can be distinguished (see text): replicates 1 11 (sums range from 116 137%), replicates 12 19 (sums range from 111 117%).

Sans serif (e.g., Arial) and non-proportional fonts (e.g., Courier) can be used to distinguish the literal text of computer programs from running text.