1. A computer-implemented system for identifying multilingual text in a document using computer processor, memory, and data storage subsystems, the computer-implemented system comprising: a code-page conversion component to identify the character encoding used by a document and to decode said encoding into a universal representative encoding via the processor; a section breaking and classification component to divide plain-text content of the document into one or more weighted sections, the sections are weighed according to their relative importance in conveying information regarding a content of the document and comprise logical areas of a layout of the document including one or more of title, heading, article text, header, and footer; a language scoring component to calculate a document score for each language present in the document by: (1) obtaining a language likelihood scores of each word, phrase, or character n-gram in the one or more weighted sections, to (2) generating a sum of the obtained language likelihood scores within the one or more weighted sections for each language present, (3) for each section, generating a summation score for each language evaluated by multiplying each language's likelihood score sum by a weight associated with the section, and (4) calculating the document score by summing the summation scores within the document; and an output language selection component to select a primary language for the document, wherein the primary language has the highest document score.
1. A computer-implemented system for identifying multilingual text in a document using computer processor, memory, and data storage subsystems, the computer-implemented system comprising: a code-page conversion component to identify the character encoding used by a document and to decode said encoding into a universal representative encoding via the processor; a section breaking and classification component to divide plain-text content of the document into one or more weighted sections, the sections are weighed according to their relative importance in conveying information regarding a content of the document and comprise logical areas of a layout of the document including one or more of title, heading, article text, header, and footer; a language scoring component to calculate a document score for each language present in the document by: (1) obtaining a language likelihood scores of each word, phrase, or character n-gram in the one or more weighted sections, to (2) generating a sum of the obtained language likelihood scores within the one or more weighted sections for each language present, (3) for each section, generating a summation score for each language evaluated by multiplying each language's likelihood score sum by a weight associated with the section, and (4) calculating the document score by summing the summation scores within the document; and an output language selection component to select a primary language for the document, wherein the primary language has the highest document score. 4. The computer-implemented system of claim 1 , wherein the plain-text content is parsed into sections based upon HTML tags, visual layout, structure, and semantic content of the document.