Computerized analysis of historical documents has remained an interesting research area for the pattern classification community for many decades. From the perspective of computerized analysis, key challenges in the historical manuscripts include automatic transcription, dating, retrieval, classification of writing styles and identification of scribes etc. Among these, the focus of our current study lies on identification of writers from the digitized manuscripts. We exploit convolutional neural networks for extraction of features and characterization of writer. The ConvNets are first trained on contemporary handwriting samples and then fine-tuned to the limited set of historical manuscripts considered in our study. Dense sampling is carried out over a given manuscript producing a set of small writing patches for each document. Decisions on patches are combined using a majority vote to conclude the authorship of a query document. Preliminary experiments on a set of challenging and degraded manuscripts report promising performance.
Learning Features for Writer Identification from Handwriting on Papyri