Extraction of Dynamic Trajectory on Multi-Stroke Static Handwriting Images Using Loop Analysis and Skeletal Graph Model

The recovery of handwriting’s dynamic stroke is an effective method to help improve the accuracy of any handwriting’s authentication or verification system. The recovered trajectory can be considered as a dynamic feature of any static handwritten images. Capitalising on this temporal information can significantly increase the accuracy of the verification phase. Extraction of dynamic features from static handwritings remains a challenge due to the lack of temporal information as compared to the online methods. Previously, there are two typical approaches to recover the handwriting’s stroke. The first approach is based on the script’s skeleton. The skeletonisation method has highly computational efficiency whereas it often produces noisy artifacts and mismatches on the resulted skeleton. The second approach deals with the handwriting’s contour, crossing areas and overlaps using parametric representations of lines and thickness of strokes. This method can avoid the artifacts, but it requires complicated mathematical models and may lead to computational explosion. Our paper is based on the script’s extracted skeleton and provides an approach to processing static handwriting’s objects, including edges, vertices and loops, as the important aspects of any handwritten image. Our paper is also to provide analysing and classifying loops types and human’s natural writing behavior to improve the global construction of stroke order. Then, a detailed tracing algorithm on global stroke reconstruction is presented. The experimental results reveal the superiority of our method as compared with the existing ones

What Knowledge about Handwritten Letters can be Used to Recover their Drawing Order ?

http://www.suvisoft.comA way to do off-line handwriting recognition is to generate an equivalent on-line signal of a letter image and to use an on-line recognition system to recognize this letter. To do this, the drawing order has to be recovered using handwriting knowledge. Our approach to recover the drawing order consists in proposing several starting and ending points. Several paths are generated and the best one is chosen. This paper presents the handwriting knowledge introduced at several steps of our method and its contribution for the recognition process. Experimentations have been carried out on isolated lower case multistroke letters from a database including both on-line and off-line signals. An on-line system has been used to recognize these letters

An examination of quantitative methods for Forensic Signature Analysis and the admissibility of signature verification system as legal evidence.

The experiments described in this thesis deal with handwriting characteristics which are involved in the production of forged and genuine signatures and complexity of signatures. The objectives of this study were (1) to provide su?cient details on which of the signature characteristics are easier to forge, (2) to investigate the capabilities of the signature complexity formula given by Found et al. based on a different signature database provided by University of Kent. This database includes the writing movements of 10 writers producing their genuine signature and of 140 writers forging these sample signatures. Using the 150 genuine signatures without constrictions of the Kent’s database an evaluation of the complexity formula suggested in Found et al took place divided the signature in three categories low, medium and high graphical complexity. The results of the formula implementation were compared with the opinions of three leading professional forensic document examiners employed by Key Forensics in the UK. The analysis of data for Study I reveals that there is not ample evidence that high quality forgeries are possible after training. In addition, a closer view of the kinematics of the forging writers is responsible for our main conclusion, that forged signatures are widely different from genuine especially in the kinematic domain. From all the parameters used in this study 11 out of 15 experienced significant changes when the comparison of the two groups (genuine versus forged signature) took place and gave a clear picture of which parameters can assist forensic document examiners and can be used by them to examine the signatures forgeries. The movements of the majority of forgers are signi?cantly slower than those of authentic writers. It is also clearly recognizable that the majority of forgers perform higher levels of pressure when trying to forge the genuine signature. The results of Study II although limited and not entirely consistent with the study of Found that proposed this model, indicate that the model can provide valuable objective evidence (regarding complex signatures) in the forensic environment and justify its further investigation but more work is need to be done in order to use this type of models in the court of law. The model was able to predict correctly only 53% of the FDEs opinion regarding the complexity of the signatures. Apart from the above investigations in this study there will be also a reference at the debate which has started in recent years that is challenging the validity of forensic handwriting experts’ skills and at the effort which has begun by interested parties of this sector to validate and standardise the field of forensic handwriting examination and a discussion started. This effort reveals that forensic document analysis field meets all factors which were set by Daubert ruling in terms of theory proven, education, training, certification, falsifiability, error rate, peer review and publication, general acceptance. However innovative methods are needed for the development of forensic document analysis discipline. Most modern and effective solution in order to prevent observational and emotional bias would be the development of an automated handwriting or signature analysis system. This system will have many advantages in real cases scenario. In addition the significant role of computer-assisted handwriting analysis in the daily work of forensic document examiners (FDE) or the judicial system is in agreement with the assessment of the National Research Council of United States that “the scientific basis for handwriting comparison needs to be strengthened”, however it seems that further research is required in order to be able these systems to reach the accomplishment point of this objective and overcome legal obstacles presented in this study

Stroke-Based Cursive Character Recognition

International audienceHuman eye can see and read what is written or displayed either in natural handwriting or in printed format. The same work in case the machine does is called handwriting recognition. Handwriting recognition can be broken down into two categories: off-line and on-line. ..

AutoGraff: towards a computational understanding of graffiti writing and related art forms.

The aim of this thesis is to develop a system that generates letters and pictures with a style that is immediately recognizable as graffiti art or calligraphy. The proposed system can be used similarly to, and in tight integration with, conventional computer-aided geometric design tools and can be used to generate synthetic graffiti content for urban environments in games and in movies, and to guide robotic or fabrication systems that can materialise the output of the system with physical drawing media. The thesis is divided into two main parts. The first part describes a set of stroke primitives, building blocks that can be combined to generate different designs that resemble graffiti or calligraphy. These primitives mimic the process typically used to design graffiti letters and exploit well known principles of motor control to model the way in which an artist moves when incrementally tracing stylised letter forms. The second part demonstrates how these stroke primitives can be automatically recovered from input geometry defined in vector form, such as the digitised traces of writing made by a user, or the glyph outlines in a font. This procedure converts the input geometry into a seed that can be transformed into a variety of calligraphic and graffiti stylisations, which depend on parametric variations of the strokes