Automatic Dating of Historical Documents

With the growing number of digitized documents available to researchers it is becoming possible to answer scientific questions by simply analyzing the image content. In this article, a new approach for the automatic dating of historical documents is proposed. It is based on an approach only recently proposed for scribe identification. It uses local RootSIFT descriptors which are encoded using VLAD. The method is evaluated using a dataset consisting of context areas of medieval papal charters covering around 150 years from 1049 to 1198 AD. Experimental results show very promising mean absolute errors of about 17 years

Unsupervised feature learning for writer identification

Our work presents a research on unsupervised feature learning methods for writer identification and retrieval. We want to study the impact of deep learning alternatives in this field by proposing methodologies which explore different uses of autoencoder networks. Taking a patch extraction algorithm as a starting point, we aim to obtain characteristics from patches of handwritten documents in an unsupervised way, meaning no label information is used for the task. To prove if the extraction of features is valid for writer identification, the approaches we propose are evaluated and compared with state-of-the-art methods on the ICDAR2013 and ICDAR2017 datasets for writer identification

WordStylist: Styled Verbatim Handwritten Text Generation with Latent Diffusion Models

Text-to-Image synthesis is the task of generating an image according to a specific text description. Generative Adversarial Networks have been considered the standard method for image synthesis virtually since their introduction; today, Denoising Diffusion Probabilistic Models are recently setting a new baseline, with remarkable results in Text-to-Image synthesis, among other fields. Aside its usefulness per se, it can also be particularly relevant as a tool for data augmentation to aid training models for other document image processing tasks. In this work, we present a latent diffusion-based method for styled text-to-text-content-image generation on word-level. Our proposed method manages to generate realistic word image samples from different writer styles, by using class index styles and text content prompts without the need of adversarial training, writer recognition, or text recognition. We gauge system performance with Frechet Inception Distance, writer recognition accuracy, and writer retrieval. We show that the proposed model produces samples that are aesthetically pleasing, help boosting text recognition performance, and gets similar writer retrieval score as real data

Writer identification using curvature-free features

Feature engineering takes a very important role in writer identification which has been widely studied in the literature. Previous works have shown that the joint feature distribution of two properties can improve the performance. The joint feature distribution makes feature relationships explicit instead of roping that a trained classifier picks up a non-linear relation present in the data. In this paper, we propose two novel and curvature-free features: run-lengths of local binary pattern (LBPruns) and cloud of line distribution (COLD) features for writer identification. The LBPruns is the joint distribution of the traditional run-length and local binary pattern (LBP) methods, which computes the run-lengths of local binary patterns on both binarized and gray scale images. The COLD feature is the joint distribution of the relation between orientation and length of line segments obtained from writing contours in handwritten documents. Our proposed LBPruns and COLD are textural-based curvature-free features and capture the line information of handwritten texts instead of the curvature information. The combination of the LBPruns and COLD features provides a significant improvement on the CERUG data set, handwritten documents on which contain a large number of irregular-curvature strokes. The results of proposed features evaluated on other two widely used data sets (Firemaker and IAM) demonstrate promising results

A theory of information processing for machine visual perception: inspiration from psychology, formal analysis and applications

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Ingeniería Informática. Fecha de lectura : 20-09-201