818 research outputs found
Text Line Segmentation of Historical Documents: a Survey
There is a huge amount of historical documents in libraries and in various
National Archives that have not been exploited electronically. Although
automatic reading of complete pages remains, in most cases, a long-term
objective, tasks such as word spotting, text/image alignment, authentication
and extraction of specific fields are in use today. For all these tasks, a
major step is document segmentation into text lines. Because of the low quality
and the complexity of these documents (background noise, artifacts due to
aging, interfering lines),automatic text line segmentation remains an open
research field. The objective of this paper is to present a survey of existing
methods, developed during the last decade, and dedicated to documents of
historical interest.Comment: 25 pages, submitted version, To appear in International Journal on
Document Analysis and Recognition, On line version available at
http://www.springerlink.com/content/k2813176280456k3
Character Recognition
Character recognition is one of the pattern recognition technologies that are most widely used in practical applications. This book presents recent advances that are relevant to character recognition, from technical topics such as image processing, feature extraction or classification, to new applications including human-computer interfaces. The goal of this book is to provide a reference source for academic research and for professionals working in the character recognition field
Arabic Handwritten Alphanumeric Character Recognition using Fuzzy Attributed Turning Functions
In this paper, we present a novel method for recognition of unconstrained handwritten Arabic alphanumeric characters. The algorithm binarizes the character image, smoothes it and extracts its contour. A novel approach for polygonal approximation of handwritten character contours is applied. The directions and length features are extracted from the polygonal approximation. These features are used to build character models in the training phase. For the recognition purpose, we introduce Fuzzy Attributed Turning Functions (FATF) and define a dissimilarity measure based on FATF for comparing polygonal shapes. Experimental results demonstrate the effectiveness of our algorithm for recognition of handwritten Arabic characters. We have obtained around 98% accuracy for Arabic handwritten characters and more than 97% accuracy for handwritten Arabic numerals
Drawing, Handwriting Processing Analysis: New Advances and Challenges
International audienceDrawing and handwriting are communicational skills that are fundamental in geopolitical, ideological and technological evolutions of all time. drawingand handwriting are still useful in defining innovative applications in numerous fields. In this regard, researchers have to solve new problems like those related to the manner in which drawing and handwriting become an efficient way to command various connected objects; or to validate graphomotor skills as evident and objective sources of data useful in the study of human beings, their capabilities and their limits from birth to decline
Human interaction with digital ink : legibility measurement and structural analysis
Literature suggests that it is possible to design and implement pen-based computer
interfaces that resemble the use of pen and paper. These interfaces appear to
allow users freedom in expressing ideas and seem to be familiar and easy to use.
Different ideas have been put forward concerning this type of interface, however
despite the commonality of aims and problems faced, there does not appear to be
a common approach to their design and implementation.
This thesis aims to progress the development of pen-based computer interfaces
that resemble the use of pen and paper. To do this, a conceptual model is proposed
for interfaces that enable interaction with "digital ink". This conceptual model is
used to organize and analyse the broad range of literature related to pen-based
interfaces, and to identify topics that are not sufficiently addressed by published
research. Two issues highlighted by the model: digital ink legibility and digital
ink structuring, are then investigated.
In the first investigation, methods are devised to objectively and subjectively
measure the legibility of handwritten script. These methods are then piloted in
experiments that vary the horizontal rendering resolution of handwritten script
displayed on a computer screen. Script legibility is shown to decrease with rendering
resolution, after it drops below a threshold value.
In the second investigation, the clustering of digital ink strokes into words is
addressed. A method of rating the accuracy of clustering algorithms is proposed:
the percentage of words spoiled. The clustering error rate is found to vary among
different writers, for a clustering algorithm using the geometric features of both
ink strokes, and the gaps between them.
The work contributes a conceptual interface model, methods of measuring
digital ink legibility, and techniques for investigating stroke clustering features, to
the field of digital ink interaction research
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Word shape analysis for a hybrid recognition system
This paper describes two wholistic recognizers developed for use in a hybrid recognition system. The recognizers use information about the word shape. This information is strongly related to word zoning. One of the recognizers is explicitly limited by the accuracy of the zoning information extraction. The other recognizer is designed so as to avoid this limitation. The recognizers use very simple sets of features and fuzzy set based pattern matching techniques. This not only aims to increase their robustness, but also causes problems with disambiguation of the results. A verification mechanism, using letter alternatives as compound features, is introduced. Letter alternatives are obtained from a segmentation based recognizer coexisting in the hybrid system. Despite some remaining disambiguation problems, wholistic recognizers are found capable of outperforming the segmentation based recognizer. When working together in a hybrid system, the results are significantly higher than that of the individual recognizers. Recognition results are reported and compared
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