Academic literature on the topic 'Writer Identification'

Writer identification is the task of associating a handwriting sample with the identity of the correct writer. It can be used to confirm or refute the authenticity of a document, or to link together documents produced by the same writer. This problem has applications in several areas, including forensics and palaeography -- the study of historical books and writings. Rigorous manual writer identification requires the exhaustive comparison of character details, and is very time-consuming, making computer automation of all or part of this process attractive. Most research into automated writer identification has originated in forensic science, although more recently applications to historical texts are increasing. With mass digitisation of texts on the rise in libraries and collections, organising this new data is a growing problem. However, different types of writing have different characteristics, and require different handling. This thesis focuses on how medieval English manuscripts from the 14th--15th centuries compare to the contemporary handwriting datasets used for much of the research and feature development in this area. The work presented here is based on an in-depth application of the grapheme codebook approach to offline writer identification. It finds domain-specific considerations throughout the process, particularly in grapheme creation and comparison and in the influence of document sources on system accuracy. Additionally, over the course of the data analysis, methods are proposed for the visualisation of extracted features, for quantifying the impact of sample source on identification accuracy, and for a nearest-neighbour-based verification system.

Malgré les prédictions d'un monde sans papier et le développement des documents électroniques, les documents manuscrits ont gardé leur importance et les problèmes de l'identification et de l'authentification des auteurs ont constitué un domaine de recherche actif au cours de ces dernières années. Nous avons développé une méthode efficace pour la reconnaissance automatique de scripteur à partir des images de texte manuscrit offline. Notre méthode repose sur deux aspects différents de l'écriture, la présence des formes redondantes dans l'écriture et des attributs visuels de l'écriture. En nous basant sur l'hypothèse qu'un individu utilise certaines formes plus fréquemment que les autres quand il écrit, nous espérons extraire ces formes en analysant des petits fragments d'écriture et en regroupant les formes similaires dans des classes. Ces classes sont déterminées soit pour chacun des scripteurs séparément ou pour un groupe de scripteurs générant un ensemble universel de formes. L'écriture en question est ensuite comparée à ces classes de formes produites. Ensuite, nous exploitons les deux importants attributs visuels de l'écriture, l'orientation et la courbure, qui permettent de distinguer une écriture d'une autre. Ces attributs sont extraits par le calcul d'un ensemble de caractéristiques à différents niveaux d'observation. Deux écritures sont ensuite comparées en calculant les distances entre leurs caractéristiques respectives. Enfin, nous combinons les deux facettes de l'écriture pour caractériser le scripteur d'un échantillon manuscrit. En utilisant ces caractéristiques, on obtient des taux d'identification qui sont comparables aux meilleurs résultats rapportés à ce jour pour l'identification de scripteur hors ligne<br>The problem of identifying the writer of a handwritten document image has been an active research area over the last few years and enjoys applications in forensic and historical document analysis. We have developed an effective method for automatic writer identification and verification from unconstrained handwritten text images. Our method relies on two different aspects of writing: the presence of redundant patterns in the writing and its visual attributes. Based on the hypothesis that handwriting carries certain patterns that an individual would use frequently as he writes, we look to extract these patterns by analyzing small writing fragments and grouping similar patterns into clusters. In fact this corresponds more to the redundancy of writing gestures than writing shapes. These clusters are determined either for each of the writers separately or, for a group of writers generating a universal set of patterns. The writing in question is then compared to the produced clusters. We next exploit two important visual attributes of writing, the orientation and curvature, which enable to distinguish one writing from another. These attributes are extracted by computing a set of features from writing samples at different levels of observation. Two writings are then compared by computing distances between their respective features. Finally, we combine the two facets of handwriting to characterize the writer of a handwritten sample. The proposed methodology, evaluated on modern as well as ancient writings exhibited promising results on tasks of writer recognition and handwriting classification

Block characters are often used when filling out forms, for example when writing ones personal number. The question of whether or not there is recoverable, biometric (identity related) information within individual digits of hand written personal numbers is then relevant. This thesis investigates the question by using both handcrafted features and extracting features via Deep learning (DL) models, and successively limiting the amount of available training samples. Some recent works using DL have presented semi-supervised methods using Generative adveserial network (GAN) generated data together with a modified Label smoothing regularization (LSR) function. Using this training method might improve performance on a baseline fully supervised model when doing authentication. This work additionally proposes a novel modified LSR function named Bootstrap label smooting regularizer (BLSR) designed to mitigate some of the problems of previous methods, and is compared to the others. The DL feature extraction is done by training a ResNet50 model to recognize writers of a personal numbers and then extracting the feature vector from the second to last layer of the network.Results show a clear indication of recoverable identity related information within the hand written (personal number) digits in boxes. Our results indicate an authentication performance, expressed in Equal error rate (EER), of around 25% with handcrafted features. The same performance measured in EER was between 20-30% when using the features extracted from the DL model. The DL methods, while showing potential for greater performance than the handcrafted, seem to suffer from fluctuation (noisiness) of results, making conclusions on their use in practice hard to draw. Additionally when using 1-2 training samples the handcrafted features easily beat the DL methods.When using the LSR variant semi-supervised methods there is no noticeable performance boost and BLSR gets the second best results among the alternatives.