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Academic literature on the topic 'FORENSICS HANDWRITTEN DOCUMENT ANALYSIS'

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Published: 11 March 2023

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Journal articles on the topic "FORENSICS HANDWRITTEN DOCUMENT ANALYSIS"

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Krasiuk, Ihor. "SIGNATURES AND SHORT HANDWRITTEN NOTES AS OBJECTS OF EXPERT ANALYSIS." Criminalistics and Forensics, no. 67 (August 9, 2022): 427–38. http://dx.doi.org/10.33994/kndise.2022.67.44.

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A detailed scientific analysis of the signature and short handwritten notes as objects of expert research has been carried out. The essence and understanding of the object of judicial handwriting examination are determined. The etymology of the terms “document”, “signature”, “short handwritten note” is investigated, the components of the object of forensic handwriting examination are established, on the basis of which the definition of the object of forensic handwriting examination is formulated as a collective concept that includes phenomena, processes and events of objective reality related to handwritten texts, as well as material carriers of information that reflect these phenomena, processes and events, which are sources of information about them and are provided for research by an expert (experts). It is concluded that “writing” and “handwriting” are close, but independent categories. They correlate with each other as a whole and part of a written functional system, are expressed in a manuscript on the basis of a written and written-motor functional-dynamic complex, have the properties of a wide and narrow profile, are complex and homogeneous objects of forensic handwriting examination. The characteristic features of the signature are determined: it is an identification mark of a certain person; performed by hand in the form of a graphic image; may consist of letters of the surname or be a conditional graphic design of the surname, initials, name; applied to a document in order to certify various facts and events. It is emphasized that the material carrier of information about the fact of the letter, its signs and other circumstances that are important in criminal proceedings, in our case, is a small handwritten product (signature and / or a short handwritten note) reflected in the corresponding document. It is proved that the signature and/or a short handwritten note are the components of the complex of objects of forensic handwriting examination. These include the material carrier itself, that is, the document in a forensic sense, as well as the phenomena, processes and events of objective reality associated with the criminal offense under investigation and reflected on the material media provided to the expert (experts) for research. Key words: small handwriting object, signature, short handwriting, object of ex- amination, forensic handwriting examination.
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Mohammed, Twana Latif, and Ahmed Abdullah Ahmed. "Offline Writer Recognition for Kurdish Handwritten Text Document Based on Proposed Codebook." UHD Journal of Science and Technology 5, no. 1 (March 31, 2021): 21–27. http://dx.doi.org/10.21928/uhdjst.v5n2y2021.pp21-27.

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Handwritten text recognition has been an ongoing attractive task to research in the field of document analysis and recognition with applications in handwriting forensics, paleography, document examination, and handwriting recognition. In the present research, an automatic method of writer recognition is presented using digitized images of unconstrained texts. Despite the increasing efforts by prior literature on the different methods used for the same purpose, such methods performance, particularly their accuracy, has not been promising, leaving plenty of room for improvements. This method made use of codebook-based writer characterization, with each writing sample represented by a group of computed features from a primary and secondary codebook. The writings were then represented through the computation of the probability of codebook patterns occurrence, and the probability distribution was employed for each writer’s characterization. Writer identification process involved comparing two writings through the computation of the distances between their respective probability distribution. The study carried out experiments to determine the performance of the implemented method in light of rates of identification with the help of standard datasets, namely, KRDOH and IAM, the former being the most current and largest Kurdish handwritten datasets with 1076 writers, and the latter being a dataset containing 650 writers. The outcome of the experiments was promising with a rate of identification of 94.3%, with the proposed method outperforming the state-of-the-art methods by 2–3%.
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Khan, Majid A., Nazeeruddin Mohammad, Ghassen Ben Brahim, Abul Bashar, and Ghazanfar Latif. "Writer verification of partially damaged handwritten Arabic documents based on individual character shapes." PeerJ Computer Science 8 (April 20, 2022): e955. http://dx.doi.org/10.7717/peerj-cs.955.

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Author verification of handwritten text is required in several application domains and has drawn a lot of attention within the research community due to its importance. Though, several approaches have been proposed for the text-independent writer verification of handwritten text, none of these have addressed the problem domain where author verification is sought based on partially-damaged handwritten documents (e.g., during forensic analysis). In this paper, we propose an approach for offline text-independent writer verification of handwritten Arabic text based on individual character shapes (within the Arabic alphabet). The proposed approach enables writer verification for partially damaged documents where certain handwritten characters can still be extracted from the damaged document. We also provide a mechanism to identify which Arabic characters are more effective during the writer verification process. We have collected a new dataset, Arabic Handwritten Alphabet, Words and Paragraphs Per User (AHAWP), for this purpose in a classroom setting with 82 different users. The dataset consists of 53,199 user-written isolated Arabic characters, 8,144 Arabic words, 10,780 characters extracted from these words. Convolutional neural network (CNN) based models are developed for verification of writers based on individual characters with an accuracy of 94% for isolated character shapes and 90% for extracted character shapes. Our proposed approach provided up to 95% writer verification accuracy for partially damaged documents.
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Islam, Ammad Ul, Muhammad Jaleed Khan, Muhammad Asad, Haris Ahmad Khan, and Khurram Khurshid. "iVision HHID: Handwritten hyperspectral images dataset for benchmarking hyperspectral imaging-based document forensic analysis." Data in Brief 41 (April 2022): 107964. http://dx.doi.org/10.1016/j.dib.2022.107964.

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Pichugin, S. A. "On the issue of new forensic methods for establishing the limitation period for the execution of document details." Courier of Kutafin Moscow State Law University (MSAL)), no. 2 (May 17, 2021): 140–46. http://dx.doi.org/10.17803/2311-5998.2021.78.2.140-146.

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The author critically examines the original forensic technique of “copying fragments of signature strokes (handwritten records) moistened with a system of solvents”. The provisions of this method contain the statement that the color of the coloring substance can be reliably determined when the studied document details were performed. The expert determines the categories of color shades visually (by eye). The comparative stage of the expert study is based on comparing the color of the dye used to make the document under study with the color scheme of the dye details of a similar method of execution, acting as comparison samples. As a result of the comparison of the colors of the dyes that were used to perform the studied details, and possibly the sample details, the expert has a personal opinion about the prescription of the studied document details. It is assumed that such a “technique” allows you to reliably establish the prescription of the details of the studied documents. The relevance of theoretical and practical analysis of the provisions of this “methodology” is associated with its active and unjustified use in solving expert problems in arbitration, civil and criminal proceedings.
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Hasan, Md Al Mehedi, Jungpil Shin, and Md Maniruzzaman. "Online Kanji Characters Based Writer Identification Using Sequential Forward Floating Selection and Support Vector Machine." Applied Sciences 12, no. 20 (October 12, 2022): 10249. http://dx.doi.org/10.3390/app122010249.

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Writer identification has become a hot research topic in the fields of pattern recognition, forensic document analysis, the criminal justice system, etc. The goal of this research is to propose an efficient approach for writer identification based on online handwritten Kanji characters. We collected 47,520 samples from 33 people who wrote 72 online handwritten-based Kanji characters 20 times. We extracted features from the handwriting data and proposed a support vector machine (SVM)-based classifier for writer identification. We also conducted experiments to see how the accuracy changes with feature selection and parameter tuning. Both text-dependent and text-independent writer identification were studied in this work. In the case of text-dependent writer identification, we obtained the accuracy of each Kanji character separately. We then studied the text-independent case by considering some of the top discriminative characters from the text-dependent case. Finally, another text-dependent experiment was performed by taking two, three, and four Kanji characters instead of using only one character. The experimental results illustrated that SVM provided the highest identification accuracy of 99.0% for the text-independent case and 99.6% for text-dependent writer identification. We hope that this study will be helpful for writer identification using online handwritten Kanji characters.
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Hora, I. V., and I. I. Popovych. "Comprehensive study of objects of judicial and technical examination of documents in solving issues regarding the establishment of their making." Uzhhorod National University Herald. Series: Law, no. 63 (August 9, 2021): 288–92. http://dx.doi.org/10.24144/2307-3322.2021.63.50.

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The article is devoted to the analysis of the essence of integrated research during the judicial and technical expertise to establish the prostitution of documents. The attention is drawn to the fact that the forensic examination with the establishment of the preparation of documents is the most difficult among other species of this kind of examinations and to be complex. The authors consider the peculiarities of this type of expertise and emphasizes that such examinations have difficulty in carrying out the use of special equipment and physicochemical research methods. The paths of determining the preparation of the paperwork are emphasized: determining the specific time of manufacture of each of the details with a further comparison of the results obtained among themselves; establishing conformity between the time of signature and handwritten records; the printing time of printing and text printing time, establish a chronological sequence of details in the document. It was concluded that the decision of the expert problem with establishing the prescription of the document requires the use of so-called "destructive methods". The main complexity of the examination of the process of making the preparation of the document is that when conducting any actions with a document (cutting, selection of ink samples), the document can be fully or partially destroyed. In this regard, expert institutions that take an obligation to carry out such examinations, pay attention to the ability to damage the document and in this connection declare a relevant request for permission to carry out such actions. Such permission should be in the document on the appointment of expertise or in the letter. Attention is drawn to the fact that not the provision of information about the time of receipt of the document may lead to the impossibility of determining the prescription of the document. After a long time and complex research process, the expert is forced to give a conclusion "is not possible."
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Rabaev, Irina, Izadeen Alkoran, Odai Wattad, and Marina Litvak. "Automatic Gender and Age Classification from Offline Handwriting with Bilinear ResNet." Sensors 22, no. 24 (December 9, 2022): 9650. http://dx.doi.org/10.3390/s22249650.

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This work focuses on automatic gender and age prediction tasks from handwritten documents. This problem is of interest in a variety of fields, such as historical document analysis and forensic investigations. The challenge for automatic gender and age classification can be demonstrated by the relatively low performances of the existing methods. In addition, despite the success of CNN for gender classification, deep neural networks were never applied for age classification. The published works in this area mostly concentrate on English and Arabic languages. In addition to Arabic and English, this work also considers Hebrew, which was much less studied. Following the success of bilinear Convolutional Neural Network (B-CNN) for fine-grained classification, we propose a novel implementation of a B-CNN with ResNet blocks. To our knowledge, this is the first time the bilinear CNN is applied for writer demographics classification. In particular, this is the first attempt to apply a deep neural network for the age classification. We perform experiments on documents from three benchmark datasets written in three different languages and provide a thorough comparison with the results reported in the literature. B-ResNet was top-ranked in all tasks. In particular, B-ResNet outperformed other models on KHATT and QUWI datasets on gender classification.
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B.M, Adeyemi, Olaoye O.J, Uchehara C.C, Akinola O.M, and Sunmola F.O. "Adoption of Off-Line Signature Verification and Forgery Detection System Using Additive Fuzzy and TS Modelling Technique in Financial Auditing and Forensics Investigation." International Journal of Computer Science and Mobile Computing 10, no. 6 (June 30, 2021): 38–59. http://dx.doi.org/10.47760/ijcsmc.2021.v10i06.004.

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This paper presents a robust signature verification and forgery detection system using Additive fuzzy and TS modeling technique. The features of various handwritten signatures are sampled with proper analysis and encapsulated to devise an effective verification system. Grid method was used to extract features angles for detection of forgeries and verification of genuine signatures. In financial Accounting, Auditing and Forensic Investigation, signature forgery could occur in various ways. This could be carried out on papers, sales documents such as invoices or inventory procurement requisition paper, title documents on landed property or other tangible assets. It is also perpetrated on payment authorization such as cheques, payment vouchers both in cash and on bills. During this exercise, the fraud perpetrators perfect their concentration on the surface paper, and trace the original signature from the mandate given earlier. It has been difficult to use accounting and auditing professions to track down financial fraud in Nigeria mostly with the problem of unearthing ingenious fraud. Exponential membership function was used to fuzzified the derived functions, and modified into structural parameters suitable to adapt to any possible variations that may result from handwriting styles and also to reflect any other factors due to scripting of a signature. The proposed system is tested on a large database of signatures obtained from 40 subjects.
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Guarnera, Luca, Giovanni Maria Farinella, Antonino Furnari, Angelo Salici, Claudio Ciampini, Vito Matranga, and Sebastiano Battiato. "Forensic analysis of handwritten documents with GRAPHJ." Journal of Electronic Imaging 27, no. 05 (July 27, 2018): 1. http://dx.doi.org/10.1117/1.jei.27.5.051230.

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Dissertations / Theses on the topic "FORENSICS HANDWRITTEN DOCUMENT ANALYSIS"

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Belvisi, Nicole Mariah Sharon. "Document Forensics Through Textual Analysis." Thesis, Högskolan i Halmstad, Akademin för informationsteknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-40157.

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This project aims at giving a brief overview of the area of research called Authorship Analysis with main focus on Authorship Attribution and the existing methods. The second objective of this project is to test whether one of the main approaches in the field can be still be applied successfully to today's new ways of communicating. The study uses multiple stylometric features to establish the authorship of a text as well as a model based on the TF-IDF model.
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Chabot, Yoan. "Construction, enrichment and semantic analysis of timelines : application to digital forensics." Thesis, Dijon, 2015. http://www.theses.fr/2015DIJOS037/document.

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Obtenir une vision précise des évènements survenus durant un incident est un objectif difficile à atteindre lors d'enquêtes de criminalistique informatique. Le problème de la reconstruction d'évènements, ayant pour objectif la construction et la compréhension d'une chronologie décrivant un incident, est l'une des étapes les plus importantes du processus d'investigation. La caractérisation et la compréhension complète d'un incident nécessite d'une part d'associer à chaque fragment d'information sa signification passée, puis d'établir des liens sémantiques entre ces fragments. Ces tâches nécessitent l'exploration de grands volumes de données hétérogènes trouvés dans la scène de crime. Face à ces masses d'informations, les enquêteurs rencontrent des problèmes de surcharge cognitive les amenant à commettre des erreurs ou à omettre des informations pouvant avoir une forte valeur ajoutée pour les progrès de l'enquête. De plus, tout résultat produit au terme de la reconstruction d'évènements doit respecter un certain nombre de critères afin de pouvoir être utilisé lors du procès. Les enquêteurs doivent notamment être en capacité d'expliquer les résultats produits. Afin d'aider les enquêteurs face à ces problèmes, cette thèse introduit l'approche SADFC. L'objectif principal de cette approche est de fournir aux enquêteurs des outils les aidant à restituer la sémantique des entités composant la scène de crime et à comprendre les relations liant ces entités tout en respectant les contraintes juridiques. Pour atteindre cet objectif, SADFC est composé de deux éléments. Tout d'abord, SADFC s'appuie sur des fondations théoriques garantissant la crédibilité des résultats produits par les outils via une définition formelle et rigoureuse des processus utilisés. Cette approche propose ensuite une architecture centrée sur une ontologie pour modéliser les connaissances inhérentes à la scène de crime et assister l'enquêteur dans l'analyse de ces connaissances. La pertinence et l'efficacité de ces outils sont démontrées au travers d'une étude relatant un cas d'investigation fictive
Having a clear view of events that occurred over time is a difficult objective to achieve in digital investigations (DI). Event reconstruction, which allows investigators to build and to understand the timeline of an incident, is one of the most important steps of a DI process. The complete understanding of an incident and its circumstances requires on the one hand to associate each piece of information to its meaning, and on the other hand to identify semantic relationships between these fragments. This complex task requires the exploration of a large and heterogeneous amount of information found on the crime scene. Therefore, investigators encounter cognitive overload problems when processing this data, causing them to make mistakes or omit information that could have a high added value for the progress of the investigation. In addition, any result produced by the reconstruction process must meet several legal requirements to be admissible at trial, including the ability to explain how the results were produced. To help the investigators to deal with these problems, this thesis introduces a semantic-based approach called SADFC. The main objective of this approach is to provide investigators with tools to help them find the meaning of the entities composing the crime scene and understand the relationships linking these entities, while respecting the legal requirements. To achieve this goal, SADFC is composed of two elements. First, SADFC is based on theoretical foundations, ensuring the credibility of the results produced by the tools via a formal and rigorous definition of the processes used. This approach then proposes an architecture centered on an ontology to model and structure the knowledge inherent to an incident and to assist the investigator in the analysis of this knowledge. The relevance and the effectiveness of this architecture are demonstrated through a case study describing a fictitious investigation
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Bosch, Campos Vicente. "Advances in Document Layout Analysis." Doctoral thesis, Universitat Politècnica de València, 2020. http://hdl.handle.net/10251/138397.

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[EN] Handwritten Text Segmentation (HTS) is a task within the Document Layout Analysis field that aims to detect and extract the different page regions of interest found in handwritten documents. HTS remains an active topic, that has gained importance with the years, due to the increasing demand to provide textual access to the myriads of handwritten document collections held by archives and libraries. This thesis considers HTS as a task that must be tackled in two specialized phases: detection and extraction. We see the detection phase fundamentally as a recognition problem that yields the vertical positions of each region of interest as a by-product. The extraction phase consists in calculating the best contour coordinates of the region using the position information provided by the detection phase. Our proposed detection approach allows us to attack both higher level regions: paragraphs, diagrams, etc., and lower level regions like text lines. In the case of text line detection we model the problem to ensure that the system's yielded vertical position approximates the fictitious line that connects the lower part of the grapheme bodies in a text line, commonly known as the baseline. One of the main contributions of this thesis, is that the proposed modelling approach allows us to include prior information regarding the layout of the documents being processed. This is performed via a Vertical Layout Model (VLM). We develop a Hidden Markov Model (HMM) based framework to tackle both region detection and classification as an integrated task and study the performance and ease of use of the proposed approach in many corpora. We review the modelling simplicity of our approach to process regions at different levels of information: text lines, paragraphs, titles, etc. We study the impact of adding deterministic and/or probabilistic prior information and restrictions via the VLM that our approach provides. Having a separate phase that accurately yields the detection position (base- lines in the case of text lines) of each region greatly simplifies the problem that must be tackled during the extraction phase. In this thesis we propose to use a distance map that takes into consideration the grey-scale information in the image. This allows us to yield extraction frontiers which are equidistant to the adjacent text regions. We study how our approach escalates its accuracy proportionally to the quality of the provided detection vertical position. Our extraction approach gives near perfect results when human reviewed baselines are provided.
[ES] La Segmentación de Texto Manuscrito (STM) es una tarea dentro del campo de investigación de Análisis de Estructura de Documentos (AED) que tiene como objetivo detectar y extraer las diferentes regiones de interés de las páginas que se encuentran en documentos manuscritos. La STM es un tema de investigación activo que ha ganado importancia con los años debido a la creciente demanda de proporcionar acceso textual a las miles de colecciones de documentos manuscritos que se conservan en archivos y bibliotecas. Esta tesis entiende la STM como una tarea que debe ser abordada en dos fases especializadas: detección y extracción. Consideramos que la fase de detección es, fundamentalmente, un problema de clasificación cuyo subproducto son las posiciones verticales de cada región de interés. Por su parte, la fase de extracción consiste en calcular las mejores coordenadas de contorno de la región utilizando la información de posición proporcionada por la fase de detección. Nuestro enfoque de detección nos permite atacar tanto regiones de alto nivel (párrafos, diagramas¿) como regiones de nivel bajo (líneas de texto principalmente). En el caso de la detección de líneas de texto, modelamos el problema para asegurar que la posición vertical estimada por el sistema se aproxime a la línea ficticia que conecta la parte inferior de los cuerpos de los grafemas en una línea de texto, comúnmente conocida como línea base. Una de las principales aportaciones de esta tesis es que el enfoque de modelización propuesto nos permite incluir información conocida a priori sobre la disposición de los documentos que se están procesando. Esto se realiza mediante un Modelo de Estructura Vertical (MEV). Desarrollamos un marco de trabajo basado en los Modelos Ocultos de Markov (MOM) para abordar tanto la detección de regiones como su clasificación de forma integrada, así como para estudiar el rendimiento y la facilidad de uso del enfoque propuesto en numerosos corpus. Así mismo, revisamos la simplicidad del modelado de nuestro enfoque para procesar regiones en diferentes niveles de información: líneas de texto, párrafos, títulos, etc. Finalmente, estudiamos el impacto de añadir información y restricciones previas deterministas o probabilistas a través de el MEV propuesto que nuestro enfoque proporciona. Disponer de un método independiente que obtiene con precisión la posición de cada región detectada (líneas base en el caso de las líneas de texto) simplifica enormemente el problema que debe abordarse durante la fase de extracción. En esta tesis proponemos utilizar un mapa de distancias que tiene en cuenta la información de escala de grises de la imagen. Esto nos permite obtener fronteras de extracción que son equidistantes a las regiones de texto adyacentes. Estudiamos como nuestro enfoque aumenta su precisión de manera proporcional a la calidad de la detección y descubrimos que da resultados casi perfectos cuando se le proporcionan líneas de base revisadas por humanos.
[CAT] La Segmentació de Text Manuscrit (STM) és una tasca dins del camp d'investigació d'Anàlisi d'Estructura de Documents (AED) que té com a objectiu detectar I extraure les diferents regions d'interès de les pàgines que es troben en documents manuscrits. La STM és un tema d'investigació actiu que ha guanyat importància amb els anys a causa de la creixent demanda per proporcionar accés textual als milers de col·leccions de documents manuscrits que es conserven en arxius i biblioteques. Aquesta tesi entén la STM com una tasca que ha de ser abordada en dues fases especialitzades: detecció i extracció. Considerem que la fase de detecció és, fonamentalment, un problema de classificació el subproducte de la qual són les posicions verticals de cada regió d'interès. Per la seva part, la fase d'extracció consisteix a calcular les millors coordenades de contorn de la regió utilitzant la informació de posició proporcionada per la fase de detecció. El nostre enfocament de detecció ens permet atacar tant regions d'alt nivell (paràgrafs, diagrames ...) com regions de nivell baix (línies de text principalment). En el cas de la detecció de línies de text, modelem el problema per a assegurar que la posició vertical estimada pel sistema s'aproximi a la línia fictícia que connecta la part inferior dels cossos dels grafemes en una línia de text, comunament coneguda com a línia base. Una de les principals aportacions d'aquesta tesi és que l'enfocament de modelització proposat ens permet incloure informació coneguda a priori sobre la disposició dels documents que s'estan processant. Això es realitza mitjançant un Model d'Estructura Vertical (MEV). Desenvolupem un marc de treball basat en els Models Ocults de Markov (MOM) per a abordar tant la detecció de regions com la seva classificació de forma integrada, així com per a estudiar el rendiment i la facilitat d'ús de l'enfocament proposat en nombrosos corpus. Així mateix, revisem la simplicitat del modelatge del nostre enfocament per a processar regions en diferents nivells d'informació: línies de text, paràgrafs, títols, etc. Finalment, estudiem l'impacte d'afegir informació i restriccions prèvies deterministes o probabilistes a través del MEV que el nostre mètode proporciona. Disposar d'un mètode independent que obté amb precisió la posició de cada regió detectada (línies base en el cas de les línies de text) simplifica enormement el problema que ha d'abordar-se durant la fase d'extracció. En aquesta tesi proposem utilitzar un mapa de distàncies que té en compte la informació d'escala de grisos de la imatge. Això ens permet obtenir fronteres d'extracció que són equidistants de les regions de text adjacents. Estudiem com el nostre enfocament augmenta la seva precisió de manera proporcional a la qualitat de la detecció i descobrim que dona resultats quasi perfectes quan se li proporcionen línies de base revisades per humans.
Bosch Campos, V. (2020). Advances in Document Layout Analysis [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/138397
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Micenková, Barbora. "Ověření pravosti razítek v dokumentu." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2011. http://www.nusl.cz/ntk/nusl-236943.

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Klasická inkoustová razítka, která se používají k autorizaci dokumentů, se dnes díky rozšíření moderních technologií dají relativně snadno padělat metodou oskenování a vytištění. V rámci diplomové práce je vyvíjen automatický nástroj pro ověření pravosti razítek, který najde využití zejména v prostředích, kde je nutné zpracovávat velké množství dokumentů. Procesu ověření pravosti razítka musí přirozeně předcházet jeho detekce v dokumentu - úloha zpracování obrazu, která zatím nemá přesvědčivé řešení. V této diplomové práci je navržena zcela nová metoda detekce a ověření pravosti razítka v barevných obrazech dokumentů. Tato metoda zahrnuje plnou segmentaci stránky za účelem určení kandidátních řešení, dále extrakci příznaků a následnou klasifikaci kandidátů za pomoci algoritmu podpůrných vektorů (SVM). Evaluace ukázala, že algoritmus umožňuje rozlišovat razítka od jiných barevných objektů v dokumentu jako jsou například loga a barevné nápisy. Kromě toho algoritmus dokáže rozlišit pravá razítka od kopií.
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Books on the topic "FORENSICS HANDWRITTEN DOCUMENT ANALYSIS"

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Handwritten Historical Document Analysis, Recognition, and Retrieval - State of the Art and Future Trends. World Scientific Publishing Co Pte Ltd, 2019.

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Book chapters on the topic "FORENSICS HANDWRITTEN DOCUMENT ANALYSIS"

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Nicolaides, Kathleen Annunziata, and Khody R. Detwiler. "Forensic Analysis of Handwritten Electronic Signatures." In Forensic Document Examination in the 21st Century, 157–72. First edition. | Boca Raton : CRC Press, 2021.: CRC Press, 2020. http://dx.doi.org/10.4324/9780367853587-17.

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Pratama, Satrya Fajri, Azah Kamilah Muda, Yun-Huoy Choo, and Noor Azilah Muda. "A New Swarm-Based Framework for Handwritten Authorship Identification in Forensic Document Analysis." In Studies in Computational Intelligence, 385–411. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05885-6_16.

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Eskenazi, Sébastien, Petra Gomez-Krämer, and Jean-Marc Ogier. "When Document Security Brings New Challenges to Document Analysis." In Computational Forensics, 104–16. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20125-2_10.

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Tüselmann, Oliver, and Gernot A. Fink. "Named Entity Linking on Handwritten Document Images." In Document Analysis Systems, 199–213. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-06555-2_14.

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Villanova-Aparisi, David, Carlos-D. Martínez-Hinarejos, Verónica Romero, and Moisés Pastor-Gadea. "Evaluation of Named Entity Recognition in Handwritten Documents." In Document Analysis Systems, 568–82. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-06555-2_38.

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Andrés, José, Alejandro H. Toselli, and Enrique Vidal. "Approximate Search for Keywords in Handwritten Text Images." In Document Analysis Systems, 367–81. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-06555-2_25.

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Andrés, José, Jose Ramón Prieto, Emilio Granell, Verónica Romero, Joan Andreu Sánchez, and Enrique Vidal. "Information Extraction from Handwritten Tables in Historical Documents." In Document Analysis Systems, 184–98. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-06555-2_13.

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Retsinas, George, Giorgos Sfikas, Basilis Gatos, and Christophoros Nikou. "Best Practices for a Handwritten Text Recognition System." In Document Analysis Systems, 247–59. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-06555-2_17.

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Legault, Raymond, Ching Y. Suen, and Christine Nadal. "Difficult Cases in Handwritten Numeral Recognition." In Structured Document Image Analysis, 235–49. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-642-77281-8_11.

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Srihari, Sargur, Anantharaman Ganesh, Catalin Tomai, Yong-Chul Shin, and Chen Huang. "Information Retrieval System for Handwritten Documents." In Document Analysis Systems VI, 298–309. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-28640-0_28.

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Conference papers on the topic "FORENSICS HANDWRITTEN DOCUMENT ANALYSIS"

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Pervouchine, Vladimir, Graham Leedham, and Konstantin Melikhov. "Handwritten character skeletonisation for forensic document analysis." In the 2005 ACM symposium. New York, New York, USA: ACM Press, 2005. http://dx.doi.org/10.1145/1066677.1066850.

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Chammas, Edgard, Chafic Mokbel, and Laurence Likforman-Sulem. "Arabic handwritten document preprocessing and recognition." In 2015 13th International Conference on Document Analysis and Recognition (ICDAR). IEEE, 2015. http://dx.doi.org/10.1109/icdar.2015.7333802.

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Majumdar, Anshuman, Praveen Krishnan, and C. V. Jawahar. "Visual Aesthetic Analysis for Handwritten Document Images." In 2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR). IEEE, 2016. http://dx.doi.org/10.1109/icfhr.2016.0085.

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Zhang, Honggang, Jun Guo, Guang Chen, and Chunguang Li. "HCL2000 - A Large-scale Handwritten Chinese Character Database for Handwritten Character Recognition." In 2009 10th International Conference on Document Analysis and Recognition. IEEE, 2009. http://dx.doi.org/10.1109/icdar.2009.15.

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Nguyen, Kim-Ngan, and Thanh-Ha Do. "Extracting Handwritten Regions In Japanese Document Images." In 2020 International Conference on Multimedia Analysis and Pattern Recognition (MAPR). IEEE, 2020. http://dx.doi.org/10.1109/mapr49794.2020.9237784.

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Indermühle, Emanuel, Marcus Liwicki, and Horst Bunke. "Combining Alignment Results for Historical Handwritten Document Analysis." In 2009 10th International Conference on Document Analysis and Recognition. IEEE, 2009. http://dx.doi.org/10.1109/icdar.2009.19.

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Awal, Ahmad-Montaser, Harold Mouchère, and Christian Viard-Gaudin. "Towards Handwritten Mathematical Expression Recognition." In 2009 10th International Conference on Document Analysis and Recognition. IEEE, 2009. http://dx.doi.org/10.1109/icdar.2009.71.

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Martin-Albo, Daniel, Veronica Romero, and Enrique Vidal. "Interactive Off-Line Handwritten Text Transcription Using On-Line Handwritten Text as Feedback." In 2013 12th International Conference on Document Analysis and Recognition (ICDAR). IEEE, 2013. http://dx.doi.org/10.1109/icdar.2013.259.

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Romero-Gomez, Veronica, Alejandro H. Toselli, Vicente Bosch, Joan Andreu Sanchez, and Enrique Vidal. "Automatic Alignment of Handwritten Images and Transcripts for Training Handwritten Text Recognition Systems." In 2018 13th IAPR International Workshop on Document Analysis Systems (DAS). IEEE, 2018. http://dx.doi.org/10.1109/das.2018.41.

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Adak, Chandranath, Bidyut B. Chaudhuri, and Michael Blumenstein. "Named Entity Recognition from Unstructured Handwritten Document Images." In 2016 12th IAPR Workshop on Document Analysis Systems (DAS). IEEE, 2016. http://dx.doi.org/10.1109/das.2016.15.

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