Handwriting based Gender identification is challenging due to unconstrained handwriting and individual differences in writing. To solve this problem, we propose a new adaptive multi-gradient of Sobel kernels for extra...
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Handwriting based Gender identification is challenging due to unconstrained handwriting and individual differences in writing. To solve this problem, we propose a new adaptive multi-gradient of Sobel kernels for extracting Adaptive Multi-Gradient Features (AMGF). For extracted text lines, the proposed method finds dominant pixels based on directional symmetry of text pixels given by AMGF. We perform histogram operation for adaptive multi-gradient values extracted corresponding to dominant pixels. The gradient values that give the highest peak in respective histograms is chosen as features. This results in feature vector having four AMGF values. The same vector are generated for successive text lines in each image to study either consistency, which is expected for females or inconsistency, which is expected for males in writing styles. The correlation is estimated based on feature vectors of the first and the successive text lines until converging or diverging criteria is met. If convergence happens, the input document is considered as female else is considered as male. The method is tested on our own dataset, which includes large variations and standard datasets, namely, QUWI, IAM-1+IAM-2 and KHATT, to demonstrate the effectiveness of the proposed method. Experimental results show that the proposed method outperforms the existing methods.
Gender identification based on handwriting analysis has received a special attention to researchers in the field of document image analysis as it is useful for several real-time applications like forensic, population ...
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Gender identification based on handwriting analysis has received a special attention to researchers in the field of document image analysis as it is useful for several real-time applications like forensic, population counting, etc. In this paper, we explore Multi-Gradient Directional (MGD) features, which provide direction of dominant pixels obtained by Canny edge image, and gradient direction symmetry. The proposed method further performs histogram operation for gradient angle information of dominant pixels of respective multi-gradient directional images to select angles, which contribute to the highest peak. This results in feature vectors. The process of feature vector formation continues for the segmented first, second, and third text lines in each image by male or female. Next, correlation is estimated for the vector of the first line with successive lines until converging or diverging criteria is met. If the convergence happens, a document is considered as by female, else is considered as by male. The method is tested on our own dataset, which includes images of different scripts, writers, papers, pens, and ages, and the standard database QUWI which includes Arabic and English texts, to demonstrate the efficiency of the proposed method. Comparative studies with the state of the art methods show that the proposed method is effective and useful.
As new digital technologies emerge to improve living style, at the same time, it also lead to increase crimes. Unlike existing approaches that use content of handwriting for fraud/forged document identification, in th...
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ISBN:
(纸本)9781509009824
As new digital technologies emerge to improve living style, at the same time, it also lead to increase crimes. Unlike existing approaches that use content of handwriting for fraud/forged document identification, in this paper we propose a novel approach that explores the quality of handwritten documents by considering both foreground and background information to identify whether it is old or new. The proposed approach works based on the fact that if a fraud document is created with some gaps after the original one, the fraud document happened to be a new one and the original happened to be an old one in this work. To identify whether a given handwritten document is old or new with gaps, we propose to divide Fourier coefficients of the input image into positive and negative coefficient images, and then reconstruct respective images to conquer two reconstructed ones. The contrast of the reconstructed images obtained before and after divide-conquer is studied to analyze the ages of the document based on image quality. The proposed approach finds a unique relationship between reconstructed images, obtained before and after divide-conquer, to identify the input image as old or new. To evaluate the proposed approach, we conduct experiments on our own handwritten dataset and a standard database, namely, Google-LIFE magazine. Comparative studies with the existing approaches show that the proposed approach outperforms the existing approaches in terms of classification rate.
As technology advances to make living comfortable for people, at the same time, different crimes also increase. One such sensitive crime is creating fake International Mobile Equipment Identity (IMEI) for smart mobile...
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As technology advances to make living comfortable for people, at the same time, different crimes also increase. One such sensitive crime is creating fake International Mobile Equipment Identity (IMEI) for smart mobile devices. In this paper, we present a new fusion based method using R, G and B color components for detecting forged IMEI numbers. To the best of our knowledge, this is the first work for forged IMEI number detection in mobile images. The proposed method first finds variances for R, G and B images of a forged input image to study local changes. The variances are used to derive weights for respective color components. The same weights are convolved with respective pixel values of R, G and B components, which results in the fused image. For the fused image, the proposed method extracts features based on sparsity, the number of connected components, and the average intensity values for edge components in respective R, G and B components, which gives six features. The proposed method finds absolute difference between fused and input images, which gives feature vector containing six difference values. The proposed method constructs templates based on samples chosen randomly. Feature vectors are compared with the templates for detecting forged IMEI numbers. Experiments are conducted on our own dataset and standard datasets to evaluate the proposed method. Furthermore, comparative studies with the related existing methods show that the proposed method outperforms the existing methods.
Script identification is an important step in multi-script document analysis. As different textures present in text portion of a script are the main distinct features of the script, in this paper, we proposed a new al...
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ISBN:
(纸本)9781479901937
Script identification is an important step in multi-script document analysis. As different textures present in text portion of a script are the main distinct features of the script, in this paper, we proposed a new algorithm for printed script identification based on texture analysis. Since local patterns is a unifying concept for traditional statistical and structural approaches of texture analysis, here the basic idea is to use the histogram of the local patterns as description of the script stroke directions distribution which is the characteristic of every script. As local pattern, the basic version of the Local Binary patterns (LBP) and a modified version of the Orientation of the Local Binary patterns (OLBP) are proposed. A Least Square Support Vector Machine (LS-SVM) is used as identifier. The scheme has been verified on two databases. The first or training database is a database with 200 sheets of 10 different scripts. The scripts font is provided by the Google translator. The second or test database has been obtained by scanning different newspapers and books. It contains 5 common scripts among 10 different scripts of the first database. From the experiment we obtained encouraging results.
Although there are advanced technologies for character recognition, automatic descriptive answer evaluation is an open challenge for the document image analysis community due to large diversified handwritten text and ...
ISBN:
(纸本)9781450397056
Although there are advanced technologies for character recognition, automatic descriptive answer evaluation is an open challenge for the document image analysis community due to large diversified handwritten text and answers to the question. This paper presents a novel method for detecting anomaly handwritten text in the responses written by the students to the questions. The method is proposed based on the fact that when the students are confident in answering questions, the students usually write answers legibly and neatly while they are not confident, they write sloppy writing which may not be easy for the reader to understand. To detect such anomaly handwritten text, we explore a new combination of Fourier transform and deep learning model for detecting edges. This result preserves the structure of handwritten text. For extracting features for classification of anomaly text and normal text, the proposed method studies the behavior of writing style, especially the variation at ascenders and descenders. Therefore, the proposed work draws principal axis which is invariant to rotation, scaling and some extent to distortion for the edge images. With respect to principal axis, the proposed method draws medial axis using uppermost and lowermost points. The distance between the medial axis and principal axis points are considered as feature vector. Further, the feature vector is passed to Artificial Neural Network for classification of anomaly text. The proposed method is evaluated by testing on our own dataset, standard dataset of gender identification (IAM) and handwritten forgery detection dataset (ACPR 2019). The results on different datasets show that the proposed work outperforms the existing methods.
Appropriate modeling of a surveillance scene is essential for detection of anomalies in road traffic. Learning usual paths can provide valuable insight into road traffic conditions and thus can help in identifying unu...
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Stochastic Gradient Decent (SGD) is one of the core techniques behind the success of deep neural networks. The gradient provides information on the direction in which a function has the steepest rate of change. The ma...
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In this paper we propose a biometric sclera recognition and validation system. Here the sclera segmentation is performed bya time-adaptive active contour-based region growing technique. The sclera vessels are not prom...
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In this paper we propose a biometric sclera recognition and validation system. Here the sclera segmentation is performed bya time-adaptive active contour-based region growing technique. The sclera vessels are not prominent so image enhancement is required and hence a bank of 2D decomposition. A Haar wavelet multi-resolution filter is used to enhance the vessels pattern for better accuracy. For feature extraction, Dense Scale Invariant Feature Transform (D-SIFT) is used. D-SIFT patch descriptors of each training image are used to form bag of features by using k-means clustering and a spatial pyramid model, which is used to produce the training model. Support Vector Machines (SVMs) are used for classification. The UBIRIS version 1 dataset is used here for experimentation. Anencouraging Equal Error Rate (EER) of 0.66% is attained in the experiments presented.
The opinion of other people is often a major factor influencing our decisions. For a consumer it affects purchase decisions and for a producer or a service provider it helps in making business decisions. Companies spe...
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The opinion of other people is often a major factor influencing our decisions. For a consumer it affects purchase decisions and for a producer or a service provider it helps in making business decisions. Companies spend a lot of money and time on surveys for gathering the public opinion on products and services. Now-a-days the web has become a hotspot for finding user opinions on almost anything under the sun. Both money and time can be saved by mining opinions from the web. Moreover, no survey can have a sample size, which can match that of the web. Each opinion generally expresses either positive, negative or neutral sentiment. The task of identifying these sentiments is called Sentiment Analysis. This work deals with the analysis of user sentiments in the Telecom domain. Since no such related standard database of users' opinions could be found, we developed one by mining the WWW. A major issue with these sample comments is that these are usually extremely noisy, containing numerous spelling and grammatical errors, acronyms, abbreviations, shortened or slang words etc. Such data cannot be used directly for analyzing sentiments. Hence, a lexicon based preprocessing algorithm is proposed for noise reduction. A novel idea based on Cosine Similarity measure is proposed for classifying the sentiment expressed by a user's comment into a five point scale of -2 (highly negative) to +2 (highly positive). The performance of the proposed strategy is compared with some of the well-known machine learning algorithms namely, Naive Bayes, Maximum Entropy and SVM. The proposed Cosine Similarity based classifier gives 82.09% accuracy for the 2-class problem of identifying positive and negative sentiments. It outperforms all other classifiers by a considerable margin in the 5-class sentiment classification problem with an accuracy of 71.5%. The same strategy is also used for categorizing each user comment into six different Telecom specific categories.
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