Oosterbroek, J (2012) Using Max-Trees with Alternative Connectivity Classes in Historical Document Processing. Master's Thesis / Essay, Artificial Intelligence.
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Abstract
In mathematical morphology, connectivity classes are a formal way of describing the grouping of sets of elements in a graph. When applied to images, connectivity classes can easily be used to describe relations in binary images and generalizes to gray-scale by means of threshold super-positioning. Connectivity classes have long been of interest in image processing research, because they provided a basis for the invention and verification of connectivity based algorithms. Much work has been invested in finding structured ways of modifying connectivity classes with predictable results. Here we present a method of using a combination of two known types of connectivity: mask and edge-based connectivity, in historical document processing. Max-Trees are data structures that can be used to express various connectivity classes. A system was built that uses a Max-Tree for all steps in the document processing chain, from preprocessing to feature generation and classification. The system aims to show that a combination of these two types of connectivity counteracts some of their mutual weaknesses. Two types of filters: the k-subtractive and k-absorption filters were used to remove noise and help with segmentation. Finally a class of features that can efficiently be computed in a Max-Tree, Normalized Central Moments, were used to classify the character zones resulting from this segmentation.
| Item Type: | Thesis (Master's Thesis / Essay) |
|---|---|
| Degree programme: | Artificial Intelligence |
| Thesis type: | Master's Thesis / Essay |
| Language: | English |
| Date Deposited: | 15 Feb 2018 07:48 |
| Last Modified: | 15 Feb 2018 07:48 |
| URI: | https://fse.studenttheses.ub.rug.nl/id/eprint/10155 |
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