Digital Image Processing
A system for machine-written and hand-written character distinction
ICDAR '95 Proceedings of the Third International Conference on Document Analysis and Recognition (Volume 2) - Volume 2
Automatic Separation of Machine-Printed and Hand-Written Text Lines
ICDAR '99 Proceedings of the Fifth International Conference on Document Analysis and Recognition
Separating Handwritten Material from Machine Printed Text Using Hidden Markov Models
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
Fractal image compression based on Delaunay triangulation and vector quantization
IEEE Transactions on Image Processing
Proceedings of the 2010 ACM Symposium on Applied Computing
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This paper describes a two level classification algorithm to discriminate the handwritten elements from the printed text in a printed document. The proposed technique is independent of size, slant, orientation, translation and other variations in handwritten text. At the first level of classification, we use two classifiers and present a comparison between the nearest neighbour classifier and Support Vector Machines(SVM) classifier to localize the handwritten text. The features that are extracted from the document are seven invariant central moments and based on these features, we classify the text as hand-written. At the second level, we use Delaunay triangulation to reclassify the misclassified elements. When Delaunay triangulation is imposed on the centroid points of the connected components, we extract features based on the triangles and reclassify the misclassified elements. We remove the noise components in the document as part of the pre-processing step.