5,116 research outputs found

    Principal Component Analysis Dimensionality Reduction For Writer Verification

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    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

    Offline Bengali writer verification by PDF-CNN and siamese net

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    © 2018 IEEE. Automated handwriting analysis is a popular area of research owing to the variation of writing patterns. In this research area, writer verification is one of the most challenging branches, having direct impact on biometrics and forensics. In this paper, we deal with offline writer verification on complex handwriting patterns. Therefore, we choose a relatively complex script, i.e., Indic Abugida script Bengali (or, Bangla) containing more than 250 compound characters. From a handwritten sample, the probability distribution functions (PDFs) of some handcrafted features are obtained and input to a convolutional neural network (CNN). For such a CNN architecture, we coin the term 'PDFCNN', where handcrafted feature PDFs are hybridized with auto-derived CNN features. Such hybrid features are then fed into a Siamese neural network for writer verification. The experiments are performed on a Bengali offline handwritten dataset of 100 writers. Our system achieves encouraging results, which sometimes exceed the results of state-of-The-Art techniques on writer verification

    Learning Representations from Persian Handwriting for Offline Signature Verification, a Deep Transfer Learning Approach

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    Offline Signature Verification (OSV) is a challenging pattern recognition task, especially when it is expected to generalize well on the skilled forgeries that are not available during the training. Its challenges also include small training sample and large intra-class variations. Considering the limitations, we suggest a novel transfer learning approach from Persian handwriting domain to multi-language OSV domain. We train two Residual CNNs on the source domain separately based on two different tasks of word classification and writer identification. Since identifying a person signature resembles identifying ones handwriting, it seems perfectly convenient to use handwriting for the feature learning phase. The learned representation on the more varied and plentiful handwriting dataset can compensate for the lack of training data in the original task, i.e. OSV, without sacrificing the generalizability. Our proposed OSV system includes two steps: learning representation and verification of the input signature. For the first step, the signature images are fed into the trained Residual CNNs. The output representations are then used to train SVMs for the verification. We test our OSV system on three different signature datasets, including MCYT (a Spanish signature dataset), UTSig (a Persian one) and GPDS-Synthetic (an artificial dataset). On UT-SIG, we achieved 9.80% Equal Error Rate (EER) which showed substantial improvement over the best EER in the literature, 17.45%. Our proposed method surpassed state-of-the-arts by 6% on GPDS-Synthetic, achieving 6.81%. On MCYT, EER of 3.98% was obtained which is comparable to the best previously reported results

    Construction and evaluation of classifiers for forensic document analysis

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    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

    Deep Adaptive Learning for Writer Identification based on Single Handwritten Word Images

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    There are two types of information in each handwritten word image: explicit information which can be easily read or derived directly, such as lexical content or word length, and implicit attributes such as the author's identity. Whether features learned by a neural network for one task can be used for another task remains an open question. In this paper, we present a deep adaptive learning method for writer identification based on single-word images using multi-task learning. An auxiliary task is added to the training process to enforce the emergence of reusable features. Our proposed method transfers the benefits of the learned features of a convolutional neural network from an auxiliary task such as explicit content recognition to the main task of writer identification in a single procedure. Specifically, we propose a new adaptive convolutional layer to exploit the learned deep features. A multi-task neural network with one or several adaptive convolutional layers is trained end-to-end, to exploit robust generic features for a specific main task, i.e., writer identification. Three auxiliary tasks, corresponding to three explicit attributes of handwritten word images (lexical content, word length and character attributes), are evaluated. Experimental results on two benchmark datasets show that the proposed deep adaptive learning method can improve the performance of writer identification based on single-word images, compared to non-adaptive and simple linear-adaptive approaches.Comment: Under view of Pattern Recognitio

    Offline Handwritten Signature Verification - Literature Review

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    The area of Handwritten Signature Verification has been broadly researched in the last decades, but remains an open research problem. The objective of signature verification systems is to discriminate if a given signature is genuine (produced by the claimed individual), or a forgery (produced by an impostor). This has demonstrated to be a challenging task, in particular in the offline (static) scenario, that uses images of scanned signatures, where the dynamic information about the signing process is not available. Many advancements have been proposed in the literature in the last 5-10 years, most notably the application of Deep Learning methods to learn feature representations from signature images. In this paper, we present how the problem has been handled in the past few decades, analyze the recent advancements in the field, and the potential directions for future research.Comment: Accepted to the International Conference on Image Processing Theory, Tools and Applications (IPTA 2017
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