A new Tsallis entropy-based thresholding algorithm for images of historical documents
Proceedings of the 2007 ACM symposium on Document engineering
A comparison of machine learning techniques for handwritten |Xam word recognition
Proceedings of the South African Institute for Computer Scientists and Information Technologists Conference
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This paper presents a study on recognition of handwritten digits extracted from images of historical documents. The work is part of ProHist, a project aimed to make easier the diffusion of historical documents. Our objective herein is to recognize the dates written in the document for indexing purposes. In this paper we investigate the influence of the feature extraction and the classification method on the performance of the recognition system. We report on experiments considering principal component analysis and undersampled bitmaps as feature extraction methods and k-nearest-neighbors, RBF neural networks and support vector machines (SVMs) as classifiers. The conclusion shows that using undersampled bitmaps combined with SVMs yield the best classification result.