Abstract
Mining historical documents provide important information that assists scientists and researchers to gain new insights into their domain. Document Layout Analysis is the first step of the automatic capturing of information from documents. The layout recognition supports establishing relationships between components in the document, which allows extraction of information. A method to visually segment critical regions of Historical handwritten documents using an object detection technique augmented with contextual features are presented in this paper. Layout analysis of Historical documents is complicated due to complex document layout and degradation of documents due to age. The object-detection technique YOLOv3 has been adopted for document layout analysis by extracting the features at different levels by down-sampling the input and combining it using Feature Pyramid Network to detect smaller regions to improve the region detection performance. The experimental results on two distinct datasets that demonstrate the method’s potential and competitive results with respect to state-of-the-art approaches have been presented.
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Ravichandra, S., Siva Sathya, S., Lourdu Marie Sophie, S. (2022). Deep Learning Based Document Layout Analysis on Historical Documents. In: Rout, R.R., Ghosh, S.K., Jana, P.K., Tripathy, A.K., Sahoo, J.P., Li, KC. (eds) Advances in Distributed Computing and Machine Learning. Lecture Notes in Networks and Systems, vol 427. Springer, Singapore. https://doi.org/10.1007/978-981-19-1018-0_23
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DOI: https://doi.org/10.1007/978-981-19-1018-0_23
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