Writer Identification in Noisy Handwritten Documents

Abstract

Identifying the writer of a handwritten document based on visual features is difficult, as evidenced by the limited number of subject matter experts proficient in forensic document analysis. Automating writer identification would be beneficial for such experts' workloads. Academic work in identifying writers has focused on clean benchmark datasets: plain white documents with uniform writing instruments. Solutions on this type of data have achieved hitin-top-10 accuracy rates reaching upwards of 98%. Unfortunately, transferring competitive techniques to handwritten documents with noise is nontrivial. This work highlights efforts in unconstrained writer identification in diverse conditions, including but not limited to lined and graph paper, coffee stains, stamps, and different writing implements. The proposed methodology blends both deep learning and traditional computer vision approaches, exploring deep convolutional neural networks (CNNs) for denoising in conjunction with hand-crafted descriptor features. Our identification algorithms are trained on existing clean datasets artificially augmented with noise, and we evaluate them on a commissioned dataset, which features a diverse but balanced set of writers, writing implements, and writing substrates (incorporating various types of noise). Experimenting with mixtures of segmentation methods, novel denoisers, specialized CNNs, and handcrafted features, we exceed the state of the art in writer identification of noisy handwritten documents by over 10%.