Heuristic based script identification from multilingual text documents

A multilingual document may contain text words in more than one language. In a multilingual country like India it is necessary that a document should be composed of text contents in different languages in order to reach a larger cross section of people, But on the other hand, this causes practical d...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: Das, M. S., Rani, D. S., Reddy, C. R. K.
Format: Tagungsbericht
Sprache:eng
Schlagworte:
Online-Zugang:Volltext bestellen
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:A multilingual document may contain text words in more than one language. In a multilingual country like India it is necessary that a document should be composed of text contents in different languages in order to reach a larger cross section of people, But on the other hand, this causes practical difficulty in OCRing such a document, because the language type of the text should be pre-determined, before employing a particular OCR (Optical Character Recognition). It is perhaps impossible to design a single recognizer which can identify a large number of scripts/languages. So, it is necessary to identify the language region of the document before feeding the document to the corresponding OCR system. Script identification aims to extract information presented in digital documents namely articles, newspapers, magazines and e-books. This has given rise to many language identification systems. The objective of this paper is to propose a model to identify script type of different text portions using visual clues. In this work seven feature namely bottom max row, top horizontal lines, vertical lines, bottom components, tick components, top holes and bottom holes have been used to identify the script type. In this work, multilingual documents with Telugu, English and Hindi scripts have been used. From the experimentation it is understood that the identification accuracy of above 93% is achieved.
DOI:10.1109/RAIT.2012.6194627