期刊名称：International Journal of Electrical and Computer Engineering
电子版ISSN：2088-8708
出版年度：2016
卷号：6
期号：5
页码：2140-2149
DOI：10.11591/ijece.v6i5.pp2140-2149
语种：English
出版社：Institute of Advanced Engineering and Science (IAES)
摘要：Detection of text orientation in document images is of preliminary concern prior to processing of documents by Optical Character Reader. The text direction in document images should exist generally in a specific orientation, i.e., text direction for any automated document reading system. The flipped text orientation leads to an unambiguous result in such fully automated systems. In this paper, we focus on development of text orientation direction detection module which can be incorporated as the perquisite process in automatic reading system. Orientation direction detection of text is performed through employing directional gradient features of document image and adapts an unsupervised learning approach for detection of flipped text orientation at which the document has been originally fed into scanning device. The unsupervised learning is built on the directional gradient features of text of document based on four possible different orientations. The algorithm is experimented on document samples of printed plain English text as well as filled in pre-printed forms of Telugu script. The outcome attained by algorithm proves to be consistent and adequate with an average accuracy around 94%.
其他摘要：Detection of text orientation in document images is of preliminary concern prior to processing of documents by Optical Character Reader. The text direction in document images should exist generally in a specific orientation, i.e., text direction for any automated document reading system. The flipped text orientation leads to an unambiguous result in such fully automated systems. In this paper, we focus on development of text orientation direction detection module which can be incorporated as the perquisite process in automatic reading system. Orientation direction detection of text is performed through employing directional gradient features of document image and adapts an unsupervised learning approach for detection of flipped text orientation at which the document has been originally fed into scanning device. The unsupervised learning is built on the directional gradient features of text of document based on four possible different orientations. The algorithm is experimented on document samples of printed plain English text as well as filled in pre-printed forms of Telugu script. The outcome attained by algorithm proves to be consistent and adequate with an average accuracy around 94%.
