Writer Retrieval and Writer Identification Using Local Features

Writer identification determines the writer of one document among a number of known writers where at least one sample is known. Writer retrieval searches all documents of one particular writer by creating a ranking of the similarity of the handwriting in a dataset. This paper presents a method for writer retrieval and writer identification using local features and therefore the proposed method is not dependent on a binarization step. First the local features of the image are calculated and with the help of a predefined codebook an occurrence histogram can be created. This histogram is compared to determine the identity of the writer or the similarity of other handwritten documents. The proposed method has been evaluated on two datasets, namely the IAM dataset which contains 650 writers and the Trigraph Slant dataset which contains 47 writers. Experiments have shown that it can keep up with previous writer identification approaches. Regarding writer retrieval it outperforms previous methods.