Pattern recognition: statistical, structural and neural approaches
Pattern recognition: statistical, structural and neural approaches
Document image analysis
A background based adaptive page segmentation algorithm
ICDAR '95 Proceedings of the Third International Conference on Document Analysis and Recognition (Volume 1) - Volume 1
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The main objective of this work is to present an approach to extract and validate the logical structure from the images that compose a commercial document. The nearest neighbor rule algorithm was used for labeling the elements, and the Run Length Smoothing Algorithm (RLSA) was used to segment the image of a commercial document of the type letter, official letter or memo. The most common classes considered are: date, logotype, text body, signature, addressee, invocation and greeting. The labeling of the elements is accomplished using the nearest neighbor rule algorithm with a vector constituted of 28 characteristics. The accomplished study presented a good result for the classification of elements on commercial documents. It was created and used a base composed of 283 images of commercial documents in 256 gray levels for the document element classification.