Personal identification based on handwriting
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TLDR
This paper attempts to eliminate the assumption that the written text is fixed by presenting a novel algorithm for automatic text-independent writer identification by taking a global approach based on texture analysis, where each writer's handwriting is regarded as a different texture.About:
This article is published in Pattern Recognition.The article was published on 2000-01-01 and is currently open access. It has received 341 citations till now. The article focuses on the topics: Handwriting & Intelligent character recognition.read more
Citations
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Proceedings ArticleDOI
Applying forensic features on writer identification
TL;DR: This work present new parameters based on biometrie handwritten information for the writer identification that will be classified by artificial neural network and fusion strategy in order to increase the accuracy.
Book ChapterDOI
Personality Trait Detection Using Handwriting Analysis by Machine Learning
TL;DR: In this paper , a system is designed to automate the basic personality trait detection tasks using handwriting analysis of graphology by using convolutional neural networks (CNNs) in order to detect personality traits.
Automated Writer Recognizer for offline Text using Scale Invariant Feature Transform Descriptor
TL;DR: The Automated writer recognizer for offline text is to determine the writer of a text among a number of known writers using their handwriting images, based on scale invariant feature transform (SIFT) descriptor.
Book ChapterDOI
Writer Identification for Handwritten Words
Shilpa Pandey,Gaurav Harit +1 more
TL;DR: This work makes use of allographic features at sub-word level to exploit the discriminative properties of features that belong to the same cluster, in a supervised approach, to achieve writer identification rates close to 63% on the handwritten words drawn from a dataset by 10 writers.
A novel scheme for word-level based offline text-independent writer identification
Vineet Kumar,Suresh Sundaram +1 more
TL;DR: A entropy-based measure to learn ‘saliency’ values of the feature maps of a particular CNN layer during the training phase of the algorithm is proposed, derived from the application of sparse PCA on the histogram of gradient features.
References
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Journal ArticleDOI
Statistical and structural approaches to texture
TL;DR: This survey reviews the image processing literature on the various approaches and models investigators have used for texture, including statistical approaches of autocorrelation function, optical transforms, digital transforms, textural edgeness, structural element, gray tone cooccurrence, run lengths, and autoregressive models.
Journal ArticleDOI
Automatic signature verification and writer identification — the state of the art
Réjean Plamondon,Guy Lorette +1 more
TL;DR: A survey of the literature on automatic signature verification and writer identification by computer, and an overview of achievements in static and dynamic approaches to solving these problems, with a special focus on preprocessing techniques, feature extraction methods, comparison processes and performance evaluation.
Journal ArticleDOI
A review of recent texture segmentation and feature extraction techniques
Todd R. Reed,J. M. Hans du Buf +1 more
TL;DR: The area of texture segmentation has undergone tremendous growth in recent years as discussed by the authors, and there has been a great deal of activity both in the refinement of previously known approaches and in the development of completely new techniques.
Journal ArticleDOI
Text segmentation using Gabor filters for automatic document processing
TL;DR: In this paper, two-dimensional Gabor filters are used to extract texture features for each text region in a given document image, and the text in the document is considered as a textured region.
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