Principal Component Analysis Dimensionality Reduction For Writer Verification

Writer verification (WV) is a process to verify whether two sample handwritten document are written by the same writer or not. WV also known as one to one comparison process, where the process is more specific which compare one writer to another writer. Therefore, this process needs a unique characteristic of the writer in order to prove the owner of the handwritten document. Basically, different person will have different type of handwriting styles usually it is unique between each other. Furthermore, most of the previous research in handwriting analysis field was used the unique characteristic to represent the individuality of handwriting. A part from that, individuality of handwriting became main issue in this study in order to fulfill requirement of WV process. In previous verification framework of WV the individuality of handwriting was acquired by using feature extraction process. Meanwhile, previous verification framework of WV consists of Preprocessing task, feature extraction task and classification task. In this study, using the previous verification framework are not enough to produce the best result in verification process. This is because the quality of individuality of handwriting that has been acquired is less effective in representing the uniqueness of the writer. Therefore, this study was proposed Dimension reduction technique for acquiring the individual features of the handwritten data henceforth improved the previous verification’s framework in order to enhance the verification accuracy. The sample data was taken from IAM online database which this database is the benchmark for handwriting analysis research. Five writers with 3619 instance of images are chosen for the experiment whereas 9 documents of handwriting samples are taken from each writer and more than 50 word randomly divided into training and testing dataset. Both dataset is will be process by Principal Component Analysis which is one of the dimension reduction techniques. PCA was applied after feature extraction process whereas the reduction process will resulted low dimensional of new subspace of data. By using the data resulted by PCA the classification process by random forest was conducted in order to verify the writer of the handwritten document. The individuality representation is implemented by presenting various representations of individual feature into more important feature are selected by using the proposed technique to be used in verifying the writer. Experimental show that the performance of the proposed methods has improved the verification rate of 90.00 % and above overall of the result with the reduction is successful in each data set. However, overall of the result the improved framework still cannot verify 100 % accurately the writer of the handwritten data

Novel geometric features for off-line writer identification

Writer identification is an important field in forensic document examination. Typically, a writer identification system consists of two main steps: feature extraction and matching and the performance depends significantly on the feature extraction step. In this paper, we propose a set of novel geometrical features that are able to characterize different writers. These features include direction, curvature, and tortuosity. We also propose an improvement of the edge-based directional and chain code-based features. The proposed methods are applicable to Arabic and English handwriting. We have also studied several methods for computing the distance between feature vectors when comparing two writers. Evaluation of the methods is performed using both the IAM handwriting database and the QUWI database for each individual feature reaching Top1 identification rates of 82 and 87 % in those two datasets, respectively. The accuracies achieved by Kernel Discriminant Analysis (KDA) are significantly higher than those observed before feature-level writer identification was implemented. The results demonstrate the effectiveness of the improved versions of both chain-code features and edge-based directional features

Construction and evaluation of classifiers for forensic document analysis

In this study we illustrate a statistical approach to questioned document examination. Specifically, we consider the construction of three classifiers that predict the writer of a sample document based on categorical data. To evaluate these classifiers, we use a data set with a large number of writers and a small number of writing samples per writer. Since the resulting classifiers were found to have near perfect accuracy using leave-one-out cross-validation, we propose a novel Bayesian-based cross-validation method for evaluating the classifiers.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS379 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Embedded Scale United Moment Invariant for Identification of Handwriting Individuality

Past few years, a lot of research on moment functions have been explored in pattern recognition. Several new techniques have been investigated to improve conventional regular moment by proposing the scaling factor of geometrical function. In this paper, integrated scaling formulations of Aspect Invariant Moment and Higher Order Scaling Invariant with United Moment Invariant are presented in Writer Identification to seek the invarianceness of authorship or individuality of handwriting perseverance. Mathematical proving and results of computer simulations are included to verify the validity of the proposed technique in identifying eccentricity of the author in Writer Identification

Embedded Scale United Moment Invariant for Identification of Handwriting Individuality

Past few years, a lot of research on moment functions have been explored in pattern recognition. Several new techniques have been investigated to improve conventional regular moment by proposing the scaling factor of geometrical function. In this paper, integrated scaling formulations of Aspect Invariant Moment and Higher Order Scaling Invariant with United Moment Invariant are presented in Writer Identification to seek the invarianceness of authorship or individuality of handwriting perseverance. Mathematical proving and results of computer simulations are included to verify the validity of the proposed technique in identifying eccentricity of the author in Writer Identification

Invariant behavioural based discrimination for individual representation

Writer identification based on cursive words is one of the extensive behavioural biometric that has involved many researchers to work in. Recently, its main idea is in forensic investigation and biometric analysis as such the handwriting style can be used as individual behavioural adaptation for authenticating an author. In this study, a novel approach of presenting cursive features of authors is presented. The invariants-based discriminability of the features is proposed by discretizing the moment features of each writer using biometric invariant discretization cutting point (BIDCP). BIDCP is introduced for features perseverance to obtain better individual representations and discriminations. Our experiments have revealed that by using the proposed method, the authorship identification based on cursive words is significantly increased with an average identification rate of 99.80%

National characteristics and variation in Arabic handwriting

From each of four Arabic countries; Morocco, Tunisia, Jordan and Oman, 150 participants produced handwriting samples which were examined to assess whether national characteristics were discernible. Ten characters, which have different configurations depending upon their position in the word, along with one short word, were classified into distinguishable forms, and these forms recorded for each handwriting sample. Tests of independence showed that character forms used were not independent of country (p < 0.001) for all but one character-position (this was dropped from subsequent analyses). A correspondence analysis ordination plot and analysis of similarity (R = 0.326, p = 0.0002) showed that whole samples were discernibly grouped by country, and a tree analysis produced a classification which was 71% accurate for the original data and 83% accurate for 80 new handwriting samples that underwent ‘blind’ classification. When the countries were combined into two regions, North Africa and Middle East, the grouping was more marked. Thus, there appears to be some scope for narrowing down the nationality, and particularly the wider geographical region of an author based upon the character forms they use in Arabic handwriting

Computer-Aided Palaeography, Present and Future

The field of digital palaeography has received increasing attention in recent years, partly because palaeographers often seem subjective in their views and do not or cannot articulate their reasoning, thereby creating a field of authorities whose opinions are closed to debate. One response to this is to make palaeographical arguments more quantitative, although this approach is by no means accepted by the wider humanities community, with some arguing that handwriting is inherently unquantifiable. This paper therefore asks how palaeographical method might be made more objective and therefore more widely accepted by non-palaeographers while still answering critics within the field. Previous suggestions for objective methods before computing are considered first, and some of their shortcomings are discussed. Similar discussion in forensic document analysis is then introduced and is found relevant to palaeography, though with some reservations. New techniques of "digital" palaeography are then introduced; these have proven successful in forensic analysis and are becoming increasingly accepted there, but they have not yet found acceptance in the humanities communities. The reasons why are discussed, and some suggestions are made for how the software might be designed differently to achieve greater acceptance. Finally, a prototype framework is introduced which is designed to provide a common basis for experiments in "digital" palaeography, ideally enabling scholars to exchange quantitative data about scribal hands, exchange processes for generating this data, articulate both the results themselves and the processes used to produce them, and therefore to ground their arguments more firmly and perhaps find greater acceptance