Character recognition—a review
Pattern Recognition
The nature of statistical learning theory
The nature of statistical learning theory
Machine-printed and hand-written text lines identification
Pattern Recognition Letters
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
The Document Spectrum for Page Layout Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Separating Handwritten Material from Machine Printed Text Using Hidden Markov Models
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
Machine Printed Text and Handwriting Identification in Noisy Document Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
A straight line detection using principal component analysis
Pattern Recognition Letters
Iterated Document Content Classification
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 01
Performance Evaluation and Benchmarking of Six-Page Segmentation Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Signature Detection and Matching for Document Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
A robust two level classification algorithm for text localization in documents
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part II
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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.