The Segmentation and Identification of Handwriting in Noisy Document Images
DAS '02 Proceedings of the 5th International Workshop on Document Analysis Systems V
Machine Printed Text and Handwriting Identification in Noisy Document Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Proceedings of the 2010 ACM Symposium on Applied Computing
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
Overlapped text segmentation using Markov random field and aggregation
DAS '10 Proceedings of the 9th IAPR International Workshop on Document Analysis Systems
Using a boosted tree classifier for text segmentation in hand-annotated documents
Pattern Recognition Letters
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Abstract: In this paper, we address the problem of separating handwritten annotations from machine printed text within a document. We present an algorithm that is based on the theory of hidden Markov models (HMM) to distinguish between machine printed and handwritten materials. No OCR results are required prior to or during the process and classification is performed on a word level. Handwritten annotations are not limited to marginal areas as the approach can deal with document images having handwritten annotations overlaying on machine printed text and shown to be promising in our experiments. Experimental results show that the proposed method can achieve 72:19% recall for fully extracted handwritten words and 90:37% for partially extracted. The precision of extracting handwritten words reaches 92:86%.