In this paper, we evaluate the performance of text- independent writer identification methods on a handwriting dataset containing medieval English documents. Applicable identification rates are achieved by combining textural features (joint directional probability distributions) with allographic features (grapheme-emission distributions). The aim is to develop an automatic handwriting identification tool that can assist the paleographer in the task of determining the authorship of historical manuscripts.
This paper presents a method for extracting rotation-invariant features from images of handwriting samples that can be used to perform writer identification. The proposed features are based on the Hinge feature , but incorporating the derivative between several points along the ink contours. Finally, we concatenate the proposed features into one feature vector to characterize the writing styles of the given handwritten text. The proposed method has been evaluated using Fire maker and IAM datasets in writer identification, showing promising performance gains.
Manuscript dating is an essential part of historical scholarship. This paper proposes a framework for image-based historical manuscript dating based on handwritten pattern analysis in scanned historical manuscript images. We first use a singular structural feature to extract the mid-level handwritten patterns in historical document images and then encode the discovered handwritten patterns based on a codebook which contains the temporal information. We evaluate our method on the Medieval Paleographic Scale (MPS) data set and experimental results demonstrate that the feature representation based on the codebook which contains temporal information is more discriminative and powerful for dating. In addition, our proposed method can also visualize the evolution of handwritten patterns over time.