Structural handwritten and machine print classification for sparse content and arbitrary oriented document fragments
Sukalpa Chanda, Katrin Franke, Umapada Pal · 2010
Discriminating handwritten and printed text is a challenging task in an arbitrary orientation scenario. The task gets even tougher when the text content is by nature sparse in the document, e.g. in torn document pieces. We here propose a system for discriminating handwritten and printed text in the context of sparse data and arbitrary orientation. A chain-code feature is used with Support Vector Machine (SVM) classifier for the purpose. Prior to feature extraction and classification some preprocessing steps (like region growing and angle estimation using Principle Component Analysis) are performed in order to resolve the arbitrary orientation issue. We got promising results of 96.90% accuracy, even when the document consists of sparse data with arbitrary orientation.