Personal identification based on handwriting
TL;DR: 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.
Citations
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01 May 2001
TL;DR: The historical evolution of CR systems is presented, the available CR techniques, with their superiorities and weaknesses, are reviewed and directions for future research are suggested.
Abstract: Character recognition (CR) has been extensively studied in the last half century and has progressed to a level that is sufficient to produce technology-driven applications. Now, rapidly growing computational power is enabling the implementation of the present CR methodologies and is creating an increasing demand in many emerging application domains which require more advanced methodologies. This paper serves as a guide and update for readers working in the CR area. First, the historical evolution of CR systems is presented. Then, the available CR techniques, with their superiorities and weaknesses, are reviewed. Finally, the current status of CR is discussed and directions for future research are suggested. Special attention is given to off-line handwriting recognition, since this area requires more research in order to reach the ultimate goal of machine simulation of human reading.
517 citations
Cites background from "Personal identification based on ha..."
...Some applications of the off-line recognition are large-scale data processing such as postal address reading [178], check sorting [94], office automation for text entry [56], automatic inspection and identification [164]....
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TL;DR: 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 are developed.
Abstract: 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
468 citations
Cites background from "Personal identification based on ha..."
...Physiological biometrics (e.g., iris, fingerprint, hand geometry, retinal blood vessels, DNA) are strong modalities for person identification due to the reduced variability and high complexity of the biometric templates used....
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TL;DR: 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, and revealed a high-sensitivity of the CO/sup 3/ PDF for identifying individual writers on the basis of a single sentence of uppercase characters.
Abstract: In this paper, a new technique for offline writer identification is presented, using connected-component contours (COCOCOs or CO/sup 3/s) 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 CO/sup 3/s 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 CO/sup 3/ 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 CO/sup 3/ PDF with an independent edge-based orientation and curvature PDF yielded very high correct identification rates.
265 citations
TL;DR: It is shown that both the writer identification and the writer verification tasks can be carried out using local features such as graphemes extracted from the segmentation of cursive handwriting, making the approach general and very promising for large scale applications in the domain of handwritten document querying and writer verification.
208 citations
TL;DR: An effective method for automatic writer recognition from unconstrained handwritten text images based on the presence of redundant patterns in the writing and its visual attributes is proposed, which exhibits promising results on writer identification and verification.
204 citations
References
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01 Jan 1979
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.
Abstract: In this survey we review the image processing literature on the various approaches and models investigators have used for texture. These include statistical approaches of autocorrelation function, optical transforms, digital transforms, textural edgeness, structural element, gray tone cooccurrence, run lengths, and autoregressive models. We discuss and generalize some structural approaches to texture based on more complex primitives than gray tone. We conclude with some structural-statistical generalizations which apply the statistical techniques to the structural primitives.
5,112 citations
"Personal identification based on ha..." refers methods in this paper
...The former is a popular method which is well recognised and the latter is often used as a benchmark in texture analysis [ 13 ]....
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...energy, entropy, contrast and correlation are computed from the matrix and used as features [ 13 ]....
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...Here two established methods are implemented to obtain texture features, namely the multichannel Gabor "ltering technique (MGF) [12] and the grey- scale co-occurrence matrix (GSCM) [ 13 ]....
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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.
981 citations
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.
Abstract: The area of texture segmentation has undergone tremendous growth in recent years. There has been a great deal of activity both in the refinement of previously known approaches and in the development of completely new techniques. Although a wide variety of methodologies have been applied to this problem, there is a particularly strong concentration in the development of feature-based approaches and on the search for appropriate texture features. In this paper, we present a survey of current texture segmentation and feature extraction methods. Our emphasis is on techniques developed since 1980, particularly those with promise for unsupervised applications.
726 citations
01 Jul 1992
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.
Abstract: There is a considerable interest in designing automatic systems that will scan a given paper document and store it on electronic media for easier storage, manipulation, and access. Most documents contain graphics and images in addition to text. Thus, the document image has to be segmented to identify the text regions, so that OCR techniques may be applied only to those regions. In this paper, we present a simple method for document image segmentation in which text regions in a given document image are automatically identified. The proposed segmentation method for document images is based on a multichannel filtering approach to texture segmentation. The text in the document is considered as a textured region. Nontext contents in the document, such as blank spaces, graphics, and pictures, are considered as regions with different textures. Thus, the problem of segmenting document images into text and nontext regions can be posed as a texture segmentation problem. Two-dimensional Gabor filters are used to extract texture features for each of these regions. These filters have been extensively used earlier for a variety of texture segmentation tasks. Here we apply the same filters to the document image segmentation problem. Our segmentation method does not assume any a priori knowledge about the content or font styles of the document, and is shown to work even for skewed images and handwritten text. Results of the proposed segmentation method are presented for several test images which demonstrate the robustness of this technique.
326 citations