A Set of Chain Code Based Features for Writer Recognition (original) (raw)
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Pattern Recognition, 2010
We propose an effective method for automatic writer recognition from unconstrained handwritten text images. Our method relies on two different aspects of writing: the presence of redundant patterns in the writing and its visual attributes. Analyzing small writing fragments, we seek to extract the patterns that an individual employs frequently as he writes. We also exploit two important visual attributes of writing, orientation and curvature, by computing a set of features from writing samples at different levels of observation. Finally we combine the two facets of handwriting to characterize the writer of a handwritten sample. The proposed methodology evaluated on two different data sets exhibits promising results on writer identification and verification.
IEEE Transactions on Pattern Analysis and …, 2004
In this paper, a new technique for offline writer identification is presented, using connected-component contours (COCOCOs or CO3s) in uppercase handwritten samples. In our model, the writer is considered to be characterized by a stochastic pattern generator, producing a family of connected components for the uppercase character set. Using a codebook of CO3s from an independent training set of 100 writers, the probability-density function (PDF) of CC's was computed for an independent test set containing 150 unseen writers. Results revealed a high-sensitivity of the CO3 PDF for identifying individual writers on the basis of a single sentence of uppercase characters. The proposed automatic approach bridges the gap between image-statistics approaches on one end and manually measured allograph features of individual characters on the other end. Combining the CO3 PDF with an independent edge-based orientation and curvature PDF yielded very high correct identification rates.
A Set of Handwriting Features for Use in Automated Writer Identification()
Journal of forensic sciences, 2017
A writer's biometric identity can be characterized through the distribution of physical feature measurements ("writer's profile"); a graph-based system that facilitates the quantification of these features is described. To accomplish this quantification, handwriting is segmented into basic graphical forms ("graphemes"), which are "skeletonized" to yield the graphical topology of the handwritten segment. The graph-based matching algorithm compares the graphemes first by their graphical topology and then by their geometric features. Graphs derived from known writers can be compared against graphs extracted from unknown writings. The process is computationally intensive and relies heavily upon statistical pattern recognition algorithms. This article focuses on the quantification of these physical features and the construction of the associated pattern recognition methods for using the features to discriminate among writers. The graph-based system d...
Text-Independent Writer Identification and Verification Using Textural and Allographic Features
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007
The identification of a person on the basis of scanned images of handwriting is a useful biometric modality with application in forensic and historic document analysis and constitutes an exemplary study area within the research field of behavioral biometrics. We developed new and very effective techniques for automatic writer identification and verification that use probability distribution functions (PDFs) extracted from the handwriting images to characterize writer individuality. A defining property of our methods is that they are designed to be independent of the textual content of the handwritten samples. Our methods operate at two levels of analysis: the texture level and the character-shape (allograph) level. At the texture level, we use contour-based joint directional PDFs that encode orientation and curvature information to give an intimate characterization of individual handwriting style. In our analysis at the allograph level, the writer is considered to be characterized by a stochastic pattern generator of ink-trace fragments, or graphemes. The PDF of these simple shapes in a given handwriting sample is characteristic for the writer and is computed using a common shape codebook obtained by grapheme clustering. Combining multiple features (directional, grapheme, and run-length PDFs) yields increased writer identification and verification performance. The proposed methods are applicable to free-style handwriting (both cursive and isolated) and have practical feasibility, under the assumption that a few text lines of handwritten material are available in order to obtain reliable probability estimates
Novel geometric features for off-line writer identification
Pattern Analysis and Applications, 2014
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 codebased 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 chaincode features and edge-based directional features.
Pattern Recognition Letters, 2011
In this paper a new approach is presented for tackling the problem of identifying the author of a handwritten text. This problem is solved with a simple, yet powerful, modification of the so called ALVOT family of supervised classification algorithms with a novel differentiated-weighting scheme. Compared to other previously published approaches, the proposed method significantly reduces the number and complexity of the text-features to be extracted from the text. Also, the specific combination of line-level and word-level features used introduces an eclectic paradigm between texture-related and structure-related approaches.► We identify a handwritten text’s author with supervised-classification techniques. ► Significantly reduced number and complexity of text-features at word and line levels. ► We use a modified ALVOT algorithm along with a novel differentiated-weighting scheme. ► Shows an eclectic paradigm between texture-related and structure-related approaches.
Text independent offline hand writer recognition using machine learning
2017
Handwriting is a behavioural biometric that an individual learns and develops over time and automated writer identification systems can be developed by identifying these behavioural aspects of an individual’s writing style. These writer recognition systems greatly assist forensic experts by facilitating them with semi-automated tools that segment the text, narrow down the search, help with visualization and finally assist in the final identification of an unknown handwritten sample. Handwriting, as a behavioural characteristic, has been a subject of interest for researchers for many decades and intensive research performed in this field has resulted in the development of multiple methods and algorithms. However, automated writer identification is still a challenging problem. Difficulties in segmenting text and the deviation of an individual from his or her unique writing style is the reason for ongoing research in this field. This thesis aims to investigate the problems faced in aut...
Offline Text-Independent Writer Identification Based on Scale Invariant Feature Transform
IEEE Transactions on Information Forensics and Security, 2014
In this paper, an efficient method for text-independent writer identification using a codebook method is proposed. The method uses the occurrence histogram of the shapes in a codebook to create a feature vector for each specific manuscript. For cursive handwritings, a wide variety of different shapes exist in the connected components obtained from the handwriting. Small fragments of connected components are used to avoid complex patterns. Two efficient methods for extracting codes from contours are introduced. One method uses the actual pixel coordinates of contour fragments while the other one uses a linear piece-wise approximation using segment angles and lengths. To evaluate the methods, writer identification is conducted on two English and three Farsi handwriting databases. Both methods show promising performances with the performance of second method being better than the first one.
Writer Identification Using Handwritten Cursive Texts and Single Character Words
Electronics
One of the biometric methods in authentication systems is the writer verification/identification using password handwriting. The main objective of this paper is to present a robust writer verification system by using cursive texts as well as block letter words. To evaluate the system, two datasets have been used. One of them is called Secure Password DB 150, which is composed of 150 users with 18 samples of single character words per user. Another dataset is public and called IAM online handwriting database, and it is composed of 220 users of cursive text samples. Each sample has been defined by a set of features, composed of 67 geometrical, statistical, and temporal features. In order to get more discriminative information, two feature reduction methods have been applied, Fisher Score and Info Gain Attribute Evaluation. Finally, the classification system has been implemented by hold-out cross validation and k-folds cross validation strategies for three different classifiers, K-NN, ...