Applying MDL to Learning Best Model Granularity

The Minimum Description Length (MDL) principle is solidly based on a provably ideal method of inference using Kolmogorov complexity. We test how the theory behaves in practice on a general problem in model selection: that of learning the best model granularity. The performance of a model depends critically on the granularity, for example the choice of precision of the parameters. Too high precision generally involves modeling of accidental noise and too low precision may lead to confusion of models that should be distinguished. This precision is often determined ad hoc. In MDL the best model is the one that most compresses a two-part code of the data set: this embodies ``Occam's Razor.'' In two quite different experimental settings the theoretical value determined using MDL coincides with the best value found experimentally. In the first experiment the task is to recognize isolated handwritten characters in one subject's handwriting, irrespective of size and orientation. Based on a new modification of elastic matching, using multiple prototypes per character, the optimal prediction rate is predicted for the learned parameter (length of sampling interval) considered most likely by MDL, which is shown to coincide with the best value found experimentally. In the second experiment the task is to model a robot arm with two degrees of freedom using a three layer feed-forward neural network where we need to determine the number of nodes in the hidden layer giving best modeling performance. The optimal model (the one that extrapolizes best on unseen examples) is predicted for the number of nodes in the hidden layer considered most likely by MDL, which again is found to coincide with the best value found experimentally.Comment: LaTeX, 32 pages, 5 figures. Artificial Intelligence journal, To appea

Preprocessing techniques for cursive script word recognition

This paper deals with techniques for improving the recognition rate of a cursive script word recognition system. Closed-loop preprocessing techniques have been designed and implemented to achieve this objective on a limited vocabulary but with no restrictions on handwriting style. This paper discusses the details of such a system and its performance on samples from several authors. Results obtained from this study are promising and suggest that closed-loop verification is a potentially more useful technique than previous open-loop processing approaches.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/25418/1/0000867.pd

Optical image scanners and character recognition devices : a survey and new taxonomy

Includes bibliographical references (p. [54]-[56]).Amar Gupta ... [et al.]

Design of an Offline Handwriting Recognition System Tested on the Bangla and Korean Scripts

This dissertation presents a flexible and robust offline handwriting recognition system which is tested on the Bangla and Korean scripts. Offline handwriting recognition is one of the most challenging and yet to be solved problems in machine learning. While a few popular scripts (like Latin) have received a lot of attention, many other widely used scripts (like Bangla) have seen very little progress. Features such as connectedness and vowels structured as diacritics make it a challenging script to recognize. A simple and robust design for offline recognition is presented which not only works reliably, but also can be used for almost any alphabetic writing system. The framework has been rigorously tested for Bangla and demonstrated how it can be transformed to apply to other scripts through experiments on the Korean script whose two-dimensional arrangement of characters makes it a challenge to recognize. The base of this design is a character spotting network which detects the location of different script elements (such as characters, diacritics) from an unsegmented word image. A transcript is formed from the detected classes based on their corresponding location information. This is the first reported lexicon-free offline recognition system for Bangla and achieves a Character Recognition Accuracy (CRA) of 94.8%. This is also one of the most flexible architectures ever presented. Recognition of Korean was achieved with a 91.2% CRA. Also, a powerful technique of autonomous tagging was developed which can drastically reduce the effort of preparing a dataset for any script. The combination of the character spotting method and the autonomous tagging brings the entire offline recognition problem very close to a singular solution. Additionally, a database named the Boise State Bangla Handwriting Dataset was developed. This is one of the richest offline datasets currently available for Bangla and this has been made publicly accessible to accelerate the research progress. Many other tools were developed and experiments were conducted to more rigorously validate this framework by evaluating the method against external datasets (CMATERdb 1.1.1, Indic Word Dataset and REID2019: Early Indian Printed Documents). Offline handwriting recognition is an extremely promising technology and the outcome of this research moves the field significantly ahead

An on-line handwritten Chinese input system using a "unique character mapping" algorithm.

by Chan Shing Chi, Michael.Thesis (M.Ph.)--Chinese University of Hong Kong, 1987.Bibliography: leaves [112]-[114

A Survey of User Interfaces for Computer Algebra Systems

AbstractThis paper surveys work within the Computer Algebra community (and elsewhere) directed towards improving user interfaces for scientific computation during the period 1963–1994. It is intended to be useful to two groups of people: those who wish to know what work has been done and those who would like to do work in the field. It contains an extensive bibliography to assist readers in exploring the field in more depth. Work related to improving human interaction with computer algebra systems is the main focus of the paper. However, the paper includes additional materials on some closely related issues such as structured document editing, graphics, and communication protocols

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

Contex-aware gestures for mixed-initiative text editings UIs

This is a pre-copyedited, author-produced PDF of an article accepted for publication in Interacting with computers following peer review. The version of record is available online at: http://dx.doi.org/10.1093/iwc/iwu019[EN] This work is focused on enhancing highly interactive text-editing applications with gestures. Concretely, we study Computer Assisted Transcription of Text Images (CATTI), a handwriting transcription system that follows a corrective feedback paradigm, where both the user and the system collaborate efficiently to produce a high-quality text transcription. CATTI-like applications demand fast and accurate gesture recognition, for which we observed that current gesture recognizers are not adequate enough. In response to this need we developed MinGestures, a parametric context-aware gesture recognizer. Our contributions include a number of stroke features for disambiguating copy-mark gestures from handwritten text, plus the integration of these gestures in a CATTI application. It becomes finally possible to create highly interactive stroke-based text-editing interfaces, without worrying to verify the user intent on-screen. We performed a formal evaluation with 22 e-pen users and 32 mouse users using a gesture vocabulary of 10 symbols. MinGestures achieved an outstanding accuracy (<1% error rate) with very high performance (<1 ms of recognition time). We then integrated MinGestures in a CATTI prototype and tested the performance of the interactive handwriting system when it is driven by gestures. Our results show that using gestures in interactive handwriting applications is both advantageous and convenient when gestures are simple but context-aware. Taken together, this work suggests that text-editing interfaces not only can be easily augmented with simple gestures, but also may substantially improve user productivity.This work has been supported by the European Commission through the 7th Framework Program (tranScriptorium: FP7- ICT-2011-9, project 600707 and CasMaCat: FP7-ICT-2011-7, project 287576). It has also been supported by the Spanish MINECO under grant TIN2012-37475-C02-01 (STraDa), and the Generalitat Valenciana under grant ISIC/2012/004 (AMIIS).Leiva, LA.; Alabau, V.; Romero Gómez, V.; Toselli, AH.; Vidal, E. (2015). Contex-aware gestures for mixed-initiative text editings UIs. Interacting with Computers. 27(6):675-696. https://doi.org/10.1093/iwc/iwu019S675696276Alabau V. Leiva L. A. Transcribing Handwritten Text Images with a Word Soup Game. Proc. Extended Abstr. Hum. Factors Comput. Syst. (CHI EA) 2012.Alabau V. Rodríguez-Ruiz L. Sanchis A. Martínez-Gómez P. Casacuberta F. On Multimodal Interactive Machine Translation Using Speech Recognition. Proc. Int. Conf. Multimodal Interfaces (ICMI). 2011a.Alabau V. Sanchis A. Casacuberta F. Improving On-Line Handwritten Recognition using Translation Models in Multimodal Interactive Machine Translation. Proc. Assoc. Comput. Linguistics (ACL) 2011b.Alabau, V., Sanchis, A., & Casacuberta, F. (2014). Improving on-line handwritten recognition in interactive machine translation. Pattern Recognition, 47(3), 1217-1228. doi:10.1016/j.patcog.2013.09.035Anthony L. Wobbrock J. O. A Lightweight Multistroke Recognizer for User Interface Prototypes. Proc. Conf. Graph. Interface (GI). 2010.Anthony L. Wobbrock J. O. N-Protractor: a Fast and Accurate Multistroke Recognizer. Proc. Conf. Graph. Interface (GI) 2012.Anthony L. Vatavu R.-D. Wobbrock J. O. Understanding the Consistency of Users' Pen and Finger Stroke Gesture Articulation. Proc. Conf. Graph. Interface (GI). 2013.Appert C. Zhai S. Using Strokes as Command Shortcuts: Cognitive Benefits and Toolkit Support. Proc. SIGCHI Conf. Hum. Fact. Comput. Syst. (CHI) 2009.Bahlmann C. Haasdonk B. Burkhardt H. On-Line Handwriting Recognition with Support Vector Machines: A Kernel Approach. Proc. Int. Workshop Frontiers Handwriting Recognition (IWFHR). 2001.Bailly G. Lecolinet E. Nigay L. Flower Menus: a New Type of Marking Menu with Large Menu Breadth, within Groups and Efficient Expert Mode Memorization. Proc.Work. Conf. Adv. Vis. Interfaces (AVI) 2008.Balakrishnan R. Patel P. The PadMouse: Facilitating Selection and Spatial Positioning for the Non-Dominant Hand. Proc. SIGCHI Conf. Hum. Factors Comput. Syst. (CHI). 1998.Bau O. Mackay W. E. Octopocus: A Dynamic Guide for Learning Gesture-Based Command Sets. Proc. ACM Symp. User Interface Softw. Technol. (UIST) 2008.Belaid A. Haton J. A syntactic approach for handwritten formula recognition. IEEE Trans. Pattern Anal. Mach. Intell. 1984;6:105-111.Bosch V. Bordes-Cabrera I. Munoz P. C. Hernández-Tornero C. Leiva L. A. Pastor M. Romero V. Toselli A. H. Vidal E. Transcribing a XVII Century Handwritten Botanical Specimen Book from Scratch. Proc. Int. Conf. Digital Access Textual Cultural Heritage (DATeCH). 2014.Buxton W. The natural language of interaction: a perspective on non-verbal dialogues. INFOR 1988;26:428-438.Cao X. Zhai S. Modeling Human Performance of Pen Stroke Gestures. Proc. SIGCHI Conf. Hum. Factors Comput. Syst. (CHI). 2007.Castro-Bleda M. J. España-Boquera S. Llorens D. Marzal A. Prat F. Vilar J. M. Zamora-Martinez F. Speech Interaction in a Multimodal Tool for Handwritten Text Transcription. Proc. Int. Conf. Multimodal Interfaces (ICMI) 2011.Connell S. D. Jain A. K. Template-based on-line character recognition. Pattern Recognition 2000;34:1-14.Costagliola G. Deufemia V. Polese G. Risi M. A Parsing Technique for Sketch Recognition Systems. Proc. 2004 IEEE Symp. Vis. Lang. Hum. Centric Comput. 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(UIST) 1991.Goldberg D. Richardson C. Touch-Typing with a Stylus. Proc. INTERCHI'93 Conf. Hum. Factors Comput. Syst. 1993.Stevens, M. E. (1968). Selected pattern recognition projects in Europe. Pattern Recognition, 1(2), 103-118. doi:10.1016/0031-3203(68)90002-2Hardock G. Design Issues for Line Driven Text Editing/ Annotation Systems. Proc. Conf. Graph. Interface (GI). 1991.Hardock G. Kurtenbach G. Buxton W. A Marking Based Interface for Collaborative Writing. Proc.ACM Symp. User Interface Softw. Technol. (UIST) 1993.Hinckley K. Baudisch P. Ramos G. Guimbretiere F. Design and Analysis of Delimiters for Selection-Action Pen Gesture Phrases in Scriboli. Proc. SIGCHI Conf. Hum. Factors Comput. Syst. (CHI). 2005.Hong J. I. Landay J. A. SATIN: A Toolkit for Informal Ink-Based Applications. Proc. ACM Symp. User Interface Softw. Technol. (UIST) 2000.Horvitz E. Principles of Mixed-Initiative User Interfaces. Proc. SIGCHI Conf. Hum. Factors Comput. Syst. (CHI). 1999.Huerst W. Yang J. Waibel A. Interactive Error Repair for an Online Handwriting Interface. Proc. SIGCHI Conf. Hum. Factors Comput. Syst. (CHI) 2010.Jelinek F. Cambridge, Massachusetts: MIT Press; 1998. Statistical Methods for Speech Recognition.Johansson S. Atwell E. Garside R. Leech G. The Tagged LOB Corpus, User's Manual. Norwegian Computing Center for the Humanities. 1996.Karat C.-M. Halverson C. Horn D. Karat J. Patterns of Entry and Correction in Large Vocabulary Continuous Speech Recognition Systems. Proc. SIGCHI Conf. Hum. Factors Comput. Syst. (CHI). 1999.Kerrick, D. D., & Bovik, A. C. (1988). Microprocessor-based recognition of handprinted characters from a tablet input. Pattern Recognition, 21(5), 525-537. doi:10.1016/0031-3203(88)90011-8Koschinski M. Winkler H. Lang M. Segmentation and Recognition of Symbols within Handwritten Mathematical Expressions. Proc. IEEE Int. Conf. Acoustics Speech Signal Process. (ICASSP). 1995.Kosmala A. Rigoll G. On-Line Handwritten Formula Recognition Using Statistical Methods. Proc. Int. Conf. Pattern Recognition (ICPR) 1998.Kristensson P. O. Discrete and continuous shape writing for text entry and control. 2007. Ph.D. Thesis, Linköping University, Sweden.Kristensson P. O. Denby L. C. Text Entry Performance of State of the Art Unconstrained Handwriting Recognition: a Longitudinal User Study. Proc. SIGCHI Conf. Hum. Factors Comput. Syst. (CHI). 2009.Kristensson P. O. Denby L. C. Continuous Recognition and Visualization of Pen Strokes and Touch-Screen Gestures. Proc. Eighth Eurograph. Symp. Sketch-Based Interfaces Model. (SBIM) 2011.Kristensson P. O. Zhai S. SHARK2: A Large Vocabulary Shorthand Writing System for Pen-Based Computers. Proc. ACM Symp. User Interface Softw. Technol. (UIST). 2004.Kurtenbach G. P. The design and evaluation of marking menus. 1991. Ph.D. Thesis, University of Toronto.Kurtenbach G. P. Buxton W. Issues in Combining Marking and Direct Manipulation Techniques. Proc. 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Visual inspection : image sampling, algorithms and architectures

The thesis concerns the hexagonal sampling of images, the processing of industrially derived images, and the design of a novel processor element that can be assembled into pipelines to effect fast, economic and reliable processing. A hexagonally sampled two dimensional image can require 13.4% fewer sampling points than a square sampled equivalent. The grid symmetry results in simpler processing operators that compute more efficiently than square grid operators. Computation savings approaching 44% arc demonstrated. New hexagonal operators arc reported including a Gaussian smoothing filter, a binary thinner, and an edge detector with comparable accuracy to that of the Sobel detector. The design of hexagonal arrays of sensors is considered. Operators requiring small local areas of support are shown to be sufficient for processing controlled lighting and industrial images. Case studies show that small features in hexagonally processed images maintain their shape better, and that processes can tolerate a lower signal to noise ratio, than that for equivalent square processed images. The modelling of small defects in surfaces has been studied in depth. The flexible programmable processor element can perform the low level local operators required for industrial image processing on both square and hexagonal grids. The element has been specified and simulated by a high level computer program. A fast communication channel allows for dynamic reprogramming by a control computer, and the video rate element can be assembled into various pipeline architectures, that may eventually be adaptively controlled