An empirical study on writer identification and verification from intra-variable individual handwriting

© 2013 IEEE. The handwriting of a person may vary substantially with factors, such as mood, time, space, writing speed, writing medium/tool, writing a topic, and so on. It becomes challenging to perform automated writer verification/identification on a particular set of handwritten patterns (e.g., speedy handwriting) of an individual, especially when the system is trained using a different set of writing patterns (e.g., normal speed) of that same person. However, it would be interesting to experimentally analyze if there exists any implicit characteristic of individuality which is insensitive to high intra-variable handwriting. In this paper, we study some handcrafted features and auto-derived features extracted from intra-variable writing. Here, we work on writer identification/verification from highly intra-variable offline Bengali writing. To this end, we use various models mainly based on handcrafted features with support vector machine and features auto-derived by the convolutional network. For experimentation, we have generated two handwritten databases from two different sets of 100 writers and enlarged the dataset by a data-augmentation technique. We have obtained some interesting results

Feasibility of Melville Marginalia Authorship Differentiation

We examine the feasibility of using image processing techniques to determine differentiation in authorship of historical pencil marks. Pencil marks with unattributed and attributed authorship are segmented from digital images of historical books. Analysis is performed on five features that are extracted from the vertical pencil marks, with those features used as a basis for authorship of marks. These marks consist of single stroke marks that are interspersed in the same document. We describe the challenges of the digital format that we were given and the steps taken in using autonomous segmentation to save pixel locations of marks. Five mark features are chosen and extracted: Average Intensity, Stroke Width, Blurriness, Stroke Curvature, and Stroke Angle. Features are then analyzed with the use of different histograms, 2D scatter plots of feature space, and comparing and contrasting the two groups of marks. C-means clustering is performed on the feature spaces of both groups. Semi-supervised clustering is used to test if we can predict the clustering. We then use two forms of cluster validity, Davies-Bouldin Index and Silhouette, in order to v produce a confidence value on the number of clusters and their membership. Then we look at the histograms and 2D scatter plots with the Melville’s Marginalia Online attributed and unattributed labels applied. Extracting features show patterns and trends within the marks that could be used to group marks. Specifically, Stroke Curvature became a dominant feature that showed promises of differentiating marks created by different authors. Extracting features has the potential to be used with high confidence in separating marks by author

How literacy acquisition affects the illiterate mind - A critical examination of theories and evidence

At present, more than one-fifth of humanity is unable to read and write. We critically examine experimental evidence and theories of how (il)literacy affects the human mind. In our discussion we show that literacy has significant cognitive consequences that go beyond the processing of written words and sentences. Thus, cultural inventions such as reading shape general cognitive processing in non-trivial ways. We suggest that this has important implications for educational policy and guidance as well as research into cognitive processing and brain functioning

Writing Development in Struggling Learners

In Writing Development in Struggling Learners, international researchers provide insights into the development of writing skills from early writing and spelling development through to composition, the reasons individuals struggle to acquire proficient writing skills and how to help these learners.; Readership: Academic libraries, graduate students; post-graduate researchers; literacy researchers; educated lay persons; literacy specialists; primary/secondary educators

Design of an Offline Handwriting Recognition System Tested on the Bangla and Korean Scripts

This dissertation presents a flexible and robust offline handwriting recognition system which is tested on the Bangla and Korean scripts. Offline handwriting recognition is one of the most challenging and yet to be solved problems in machine learning. While a few popular scripts (like Latin) have received a lot of attention, many other widely used scripts (like Bangla) have seen very little progress. Features such as connectedness and vowels structured as diacritics make it a challenging script to recognize. A simple and robust design for offline recognition is presented which not only works reliably, but also can be used for almost any alphabetic writing system. The framework has been rigorously tested for Bangla and demonstrated how it can be transformed to apply to other scripts through experiments on the Korean script whose two-dimensional arrangement of characters makes it a challenge to recognize. The base of this design is a character spotting network which detects the location of different script elements (such as characters, diacritics) from an unsegmented word image. A transcript is formed from the detected classes based on their corresponding location information. This is the first reported lexicon-free offline recognition system for Bangla and achieves a Character Recognition Accuracy (CRA) of 94.8%. This is also one of the most flexible architectures ever presented. Recognition of Korean was achieved with a 91.2% CRA. Also, a powerful technique of autonomous tagging was developed which can drastically reduce the effort of preparing a dataset for any script. The combination of the character spotting method and the autonomous tagging brings the entire offline recognition problem very close to a singular solution. Additionally, a database named the Boise State Bangla Handwriting Dataset was developed. This is one of the richest offline datasets currently available for Bangla and this has been made publicly accessible to accelerate the research progress. Many other tools were developed and experiments were conducted to more rigorously validate this framework by evaluating the method against external datasets (CMATERdb 1.1.1, Indic Word Dataset and REID2019: Early Indian Printed Documents). Offline handwriting recognition is an extremely promising technology and the outcome of this research moves the field significantly ahead

Template Based Recognition of On-Line Handwriting

Software for recognition of handwriting has been available for several decades now and research on the subject have produced several different strategies for producing competitive recognition accuracies, especially in the case of isolated single characters. The problem of recognizing samples of handwriting with arbitrary connections between constituent characters (emph{unconstrained handwriting}) adds considerable complexity in form of the segmentation problem. In other words a recognition system, not constrained to the isolated single character case, needs to be able to recognize where in the sample one letter ends and another begins. In the research community and probably also in commercial systems the most common technique for recognizing unconstrained handwriting compromise Neural Networks for partial character matching along with Hidden Markov Modeling for combining partial results to string hypothesis. Neural Networks are often favored by the research community since the recognition functions are more or less automatically inferred from a training set of handwritten samples. From a commercial perspective a downside to this property is the lack of control, since there is no explicit information on the types of samples that can be correctly recognized by the system. In a template based system, each style of writing a particular character is explicitly modeled, and thus provides some intuition regarding the types of errors (confusions) that the system is prone to make. Most template based recognition methods today only work for the isolated single character recognition problem and extensions to unconstrained recognition is usually not straightforward. This thesis presents a step-by-step recipe for producing a template based recognition system which extends naturally to unconstrained handwriting recognition through simple graph techniques. A system based on this construction has been implemented and tested for the difficult case of unconstrained online Arabic handwriting recognition with good results

THE RELATIONSHIP BETWEEN GRAMMAR ABILITY AND READING COMPREHENSION ON FOURTH SEMESTER OF ENGLISH EDUCATION STUDENTS IN YOGYAKARTA STATE UNIVERSITY IN THE ACADEMIC YEAR OF 2012/2013

The aim of this research is to find out whether there is a positive and significant relationship between grammar ability in terms of: subject – predicate construction, noun phrase understanding and word recognition and reading comprehension ability partially and in aggregate among fourth semester students of English Education Department in Yogyakarta State University in the academic year of 2012/2013. This type of this research was non-experimental research study or in other words ex-post facto research. The steps of this research include instrument try-out test and tests. The population of this research was 113 students, and the researcher took 30 students for the research instrument try-out. Meanwhile, in taking sample, it tested 86 students. The research involved quantitative data. It was obtained by instrument try out and a test. To get trustworthiness, the research not only applied validity and reliability test but also used data analysis techniques which consisted of descriptive and inferential analyses. From the actions conducted, it is concluded that there is a positive and significant relationship between grammar ability and reading ability partially in terms of: subject – predicate construction, in which the r0 (r-obtained = 0.362) which is higher than the rt (r-table = 0.213) at the level of significance of 5 %; noun phrase understanding and reading comprehension ability, in which by the r0 (r-obtained = 0.571) which is higher than the rt (r-table = 0.213) at the level of significance of 5 %, and word recognition and reading comprehension ability in which the r0 (r-obtained = 0.547) which is higher than the rt (r-table = 0.213) at the level of significance of 5 %. The data also proved that there is a positive and significant correlation between subject predicate construction ability, noun phrase understanding and word recognition ability in aggregate with reading comprehension ability, in which the r0 (r-obtained = 0.540) which is higher than the rt (r-table = 0.213) at the level of significance of 5 %

Writing Development in Struggling Learners

In Writing Development in Struggling Learners, international researchers provide insights into the development of writing skills from early writing and spelling development through to composition, the reasons individuals struggle to acquire proficient writing skills and how to help these learners.; Readership: Academic libraries, graduate students; post-graduate researchers; literacy researchers; educated lay persons; literacy specialists; primary/secondary educators

Writing Around the Ancient Mediterranean

Writing in the ancient Mediterranean existed against a backdrop of very high levels of interaction and contact. In the societies around its shores, writing was a dynamic practice that could serve many purposes – from a tool used by elites to control resources and establish their power bases to a symbol of local identity and a means of conveying complex information and ideas. This volume presents a group of papers by members of the Contexts of and Relations between Early Writing Systems (CREWS) research team and visiting fellows, offering a range of different perspectives and approaches to problems of writing in the ancient Mediterranean. They focus on practices, viewing writing as something that people do within a wider social and cultural context, and on adaptations, considering the ways in which writing changed and was changed by the people using it

Script and Society

By the 13th century BC, the Syrian city of Ugarit hosted an extremely diverse range of writing practices. As well as two main scripts – alphabetic and logographic cuneiform - the site has also produced inscriptions in a wide range of scripts and languages, including Hurrian, Sumerian, Hittite, Egyptian hieroglyphs, Luwian hieroglyphs and Cypro-Minoan. This variety in script and language is accompanied by writing practices that blend influences from Mesopotamian, Anatolian and Levantine traditions together with what seem to be distinctive local innovations. Script and Society: The Social Context of Writing Practices in Late Bronze Age Ugarit explores the social and cultural context of these complex writing traditions from the perspective of writing as a social practice. It combines archaeology, epigraphy, history and anthropology to present a highly interdisciplinary exploration of social questions relating to writing at the site, including matters of gender, ethnicity, status and other forms of identity, the relationship between writing and place, and the complex relationships between inscribed and uninscribed objects. This forms a case- study for a wider discussion of interdisciplinary approaches to the study of writing practices in the ancient world