Automatic handwriter identification using advanced machine learning

Handwriter identification a challenging problem especially for forensic investigation. This topic has received significant attention from the research community and several handwriter identification systems were developed for various applications including forensic science, document analysis and investigation of the historical documents. This work is part of an investigation to develop new tools and methods for Arabic palaeography, which is is the study of handwritten material, particularly ancient manuscripts with missing writers, dates, and/or places. In particular, the main aim of this research project is to investigate and develop new techniques and algorithms for the classification and analysis of ancient handwritten documents to support palaeographic studies. Three contributions were proposed in this research. The first is concerned with the development of a text line extraction algorithm on colour and greyscale historical manuscripts. The idea uses a modified bilateral filtering approach to adaptively smooth the images while still preserving the edges through a nonlinear combination of neighboring image values. The proposed algorithm aims to compute a median and a separating seam and has been validated to deal with both greyscale and colour historical documents using different datasets. The results obtained suggest that our proposed technique yields attractive results when compared against a few similar algorithms. The second contribution proposes to deploy a combination of Oriented Basic Image features and the concept of graphemes codebook in order to improve the recognition performances. The proposed algorithm is capable to effectively extract the most distinguishing handwriter’s patterns. The idea consists of judiciously combining a multiscale feature extraction with the concept of grapheme to allow for the extraction of several discriminating features such as handwriting curvature, direction, wrinkliness and various edge-based features. The technique was validated for identifying handwriters using both Arabic and English writings captured as scanned images using the IAM dataset for English handwriting and ICFHR 2012 dataset for Arabic handwriting. The results obtained clearly demonstrate the effectiveness of the proposed method when compared against some similar techniques. The third contribution is concerned with an offline handwriter identification approach based on the convolutional neural network technology. At the first stage, the Alex-Net architecture was employed to learn image features (handwritten scripts) and the features obtained from the fully connected layers of the model. Then, a Support vector machine classifier is deployed to classify the writing styles of the various handwriters. In this way, the test scripts can be classified by the CNN training model for further classification. The proposed approach was evaluated based on Arabic Historical datasets; Islamic Heritage Project (IHP) and Qatar National Library (QNL). The obtained results demonstrated that the proposed model achieved superior performances when compared to some similar method

Automatic intrapersonal variability modeling for offline signature augmentation

Orientador: Luiz Eduardo Soares de OliveiraCoorientadores: Robert Sabourin e Alceu de Souza Britto Jr..Tese (doutorado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Programa de Pós-Graduação em Informática. Defesa : Curitiba, 19/07/2021Inclui referências: p. 93-102Área de concentração: Ciência da ComputaçãoResumo: Normalmente, em um cenario do mundo real, poucas assinaturas estao disponiveis para treinar um sistema de verificacao automatica de assinaturas (SVAA). Para resolver esse problema, diversas abordagens para a duplicacao de assinaturas estaticas foram propostas ao longo dos anos. Essas abordagens geram novas amostras de assinaturas sinteticas aplicando algumas transformacoes na imagem original da assinatura. Algumas delas geram amostras realistas, especialmente o duplicator. Este metodo utiliza um conjunto de parametros para modelar o comportamento do escritor (variabilidade do escritor) ao assinar. No entanto, esses parametros so empiricamente definidos. Este tipo de abordagem pode ser demorado e pode selecionar parametros que nao descrevem a real variabilidade do escritor. A principal hipotese desse trabalho e que a variabilidade do escritor observada no dominio da imagem tambem pode ser transferido para o dominio de caracteristicas. Portanto, este trabalho propoe um novo metodo para modelar automaticamente a variabilidade do escritor para a posterior duplicacao de assinaturas no dominio de imagem (duplicator) e dominio de caracteristicas (filtro Gaussiano e variacao do metodo de Knop). Este trabalho tambem propoe um novo metodo de duplicacao de assinaturas estaticas, que gera as amostras sinteticas diretamente no dominio de caracteristicas usando um filtro Gaussiano. Alem disso, uma nova abordagem para avaliar a qualidade de amostras sinteticas no dominio de caracteristicas e apresentada. As limitacoes e vantagens de ambas as abordagens de duplicacao de assinaturas tambem sao exploradas. Alem de usar a nova abordagem para avaliar a qualidade das amostras, o desempenho de um SVAA e avaliado usando as amostras e tres bases de assinaturas estaticas bem conhecidas: a GPDS-300, a MCYT-75 e a CEDAR. Para a mais utilizada, GPDS-300, quando o classificador SVM foi treinando com somente uma assinatura genuina por escritor, ele obteve um Equal Error Rate (EER) de 5,71%. Quando o classificador tambem utilizou as amostras sinteticas geradas no dominio de imagem, o EER caiu para 1,08%. Quando o classificador foi treinado com as amostras geradas pelo filtro Gaussiano, o EER caiu para 1,04%.Abstract: Normally, in a real-world scenario, there are few signatures available to train an automatic signature verification system (ASVS). To address this issue, several offline signature duplication approaches have been proposed along the years. These approaches generate a new synthetic signature sample applying some transformations in the original signature image. Some of them generate realistic samples, specially the duplicator. This method uses a set of parameters to model the writer's behavior (writer variability) during the signing act. However, these parameters are empirically defined. This kind of approach can be time consuming and can select parameters that do not describe the real writer variability. The main hypothesis of this work is that the writer variability observed in the image space can be transferred to the feature space as well. Therefore, this work proposes a new method to automatically model the writer variability for further signature duplication in the image (duplicator) and the feature space (Gaussian filter and a variation of Knop's method). This work also proposes a new offline signature duplication method, which directly generates the synthetic samples in the feature space using a Gaussian filter. Furthermore, a new approach to assess the quality of the synthetic samples in the feature space is introduced. The limitations and advantages of both signature augmentation approaches are also explored. Despite using the new approach to assess the quality of the samples, the performance of an ASVS was assessed using them and three well-known offline signature datasets: GPDS-300, MCYT-75, and CEDAR. For the most used one, GPDS-300, when the SVM classifier was trained with only one genuine signature per writer, it achieved an Equal Error Rate (EER) of 5.71%. When the classifier also was trained with the synthetic samples generated in the image space, the EER dropped to 1.08%. When the classifier was trained using the synthetic samples generated by the Gaussian filter, the EER dropped to 1.04%

Offline signature verification using writer-dependent ensembles and static classifier selection with handcraft features

Orientador: Eduardo TodtDissertação (mestrado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Programa de Pós-Graduação em Informática. Defesa : Curitiba, 17/02/2022Inclui referências: p. 85-94Área de concentração: Ciência da ComputaçãoResumo: Reconhecimento e identificação de assinaturas em documentos e manuscritos são tarefas desafiadoras que ao longo do tempo vêm sendo estudadas, em especial na questão de discernir assinaturas genuínas de falsificações. Com o recente avanço das tecnologias, principalmente no campo da computação, pesquisas nesta área têm se tornado cada vez mais frequentes, possibilitando o uso de novos métodos de análise das assinaturas, aumentando a precisão e a confiança na verificação delas. Ainda há muito o que se explorar em pesquisas desta área dentro da computação. Verificações de assinaturas consistem, de forma geral, em obter características acerca de um a assinatura e utilizá-las para discerni-la das demais. Estudos propondo variados tipos de métodos foram realizados nos últimos anos a fim de aprimorar os resultados obtidos por sistemas de verificação e identificação de assinaturas. Diferentes formas de extrair características têm sido exploradas, com o o uso de redes neurais artificiais voltadas especificam ente para verificação de assinaturas, como a ResNet e a SigNet, representando o estado-da-arte nesta área de pesquisa. Apesar disso, métodos mais simples de extração de características ainda são muito utilizados, como o histograma de gradientes orientados (HOG), o Local Binary Patterns (LBP) e Local Phase Quantization (LPQ) por exemplo, apresentando, em muitos casos, resultados similares ao estado-da-arte. Não apenas isso, mas diferentes formas de combinar informações de extratores de características e resultados de classificadores têm sido propostos, como é o caso dos seletores de características, métodos de comitê de máquinas e algoritmos de análise da qualidade das características. D esta form a, o trabalho realizado consiste em explorar diferentes métodos de extração de características com binados em um conjunto de classificadores, de maneira que cada conjunto seja construído de forma dependente do autor e seja especificam ente adaptado para reconhecer as melhores características para cada autor, aprendendo quais com binações de classificadores com determinado grupo de características melhor se adaptam para reconhecer suas assinaturas. O desempenho e a funcionalidade do sistema foram comparados com os principais trabalhos da área desenvolvidos nos últimos anos, tendo sido realizados testes com as databases CEDAR, M CYT e UTSig. A pesar de não superar o estado-da-arte, o sistema apresentou bom desempenho, podendo ser com parado com alguns outros trabalhos importantes na área. Além disso, o sistema mostrou a eficiência dos classificadores Support Vector M achine(SVM ) e votadores para a realização da meta-classificação, bem como o potencial de alguns extratores de características para a área de verificação de assinaturas, com o foi o caso do Compound Local Binary Pattern(CLBP).Abstract: Signature recognition and identification in documents and manuscripts are challenging tasks that have been studied over time, especially in the matter of discerning genuine signatures from forgeries. With the recent advancement of technologies, especially in the field of computing, research in this area has become increasingly frequent, enabling the use of new methods of analysis of signatures, increasing accuracy and confidence in their verification. There is still much to be explored in research in this area within computing. Signature verification generally consists in obtaining features about a signature and using them to distinguish it from others. Studies proposing different types o f methods have been carried out in recent years in order to improve the results obtained by signature verification and identification systems. Different ways of extracting features have been explored, such as the use of artificial neural networks specifically aimed at verifying signatures, like ResNet and SigNet, representing the state-of-the-art in this research area. Despite this, simpler methods of feature extraction are still widely used, such as the Histogram of Oriented Gradients (HOG), the Local Binary Patterns (LBP) and the Local Phase Quantization (LPQ) for example, presenting, in many cases, similar results to the state-of-the-art. Not only that, but different ways of combining information from feature extractors and results from classifiers have been proposed, such as feature selectors, machine committee methods and feature quality analysis algorithms. In this way, the developed work consists in exploring different methods of features extractors combined in an ensemble, so that each ensemble is built in a writer-dependent way and is specifically adapted to recognize the best features for each author, learning which combinations of classifiers with a certain group of characteristics is better adapted to recognize their signatures. The performance and functionality of the system were compared w ith the m ain works in the area developed in recent years, w ith tests having been carried out with the CEDAR, M CYT and UTSig databases. Despite not overcoming the state-of-the-art, the system presented good performance, being able to be compared with some other important works in the area. In addition, the system showed the efficiency of Support Vector Machine(SVM ) classifiers and voters to perform the meta-classification, as well as the potential of some feature extractors for the signature verification area, such as the Compound Local Binary Pattern(CLBP)

2022 SDSU Data Science Symposium Presentation Abstracts

This document contains abstracts for presentations and posters 2022 SDSU Data Science Symposium

2022 SDSU Data Science Symposium Presentation Abstracts

This document contains abstracts for presentations and posters 2022 SDSU Data Science Symposium