Abstract
It is presented herein a new entropy-based segmentation algorithm for images of historical documents. The algorithm provides high quality images and it also improves OCR (Optical Character Recognition) responses for typed documents. It adapts its settings to achieve better quality images through changes in the logarithmic base that defines entropy. For this purpose, a measure for image fidelity is applied just as information inherent to images of documents.
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de Mello, C.A.B. (2004). Image Segmentation of Historical Documents: Using a Quality Index. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2004. Lecture Notes in Computer Science, vol 3212. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30126-4_26
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DOI: https://doi.org/10.1007/978-3-540-30126-4_26
Publisher Name: Springer, Berlin, Heidelberg
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