A survey of document image classification: problem statement,classifier architecture and performance evaluation |
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Authors: | Nawei Chen Dorothea Blostein |
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Affiliation: | (1) School of Computing, Queen’s University, K7L 3N6 Kingston, ON, Canada |
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Abstract: | Document image classification is an important step in Office Automation, Digital Libraries, and other document image analysis
applications. There is great diversity in document image classifiers: they differ in the problems they solve, in the use of
training data to construct class models, and in the choice of document features and classification algorithms. We survey this
diverse literature using three components: the problem statement, the classifier architecture, and performance evaluation.
This brings to light important issues in designing a document classifier, including the definition of document classes, the
choice of document features and feature representation, and the choice of classification algorithm and learning mechanism.
We emphasize techniques that classify single-page typeset document images without using OCR results. Developing a general,
adaptable, high-performance classifier is challenging due to the great variety of documents, the diverse criteria used to
define document classes, and the ambiguity that arises due to ill-defined or fuzzy document classes. |
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Keywords: | Document image classification Document classifiers Document classification Document categorization Document features Feature representations Class models Classification algorithms Learning mechanisms Performance evaluation |
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