Handwriting recognition in intelligent design systems

• Autorzy: Kacalak W. , Majewski M.

Warianty tytułu

PL

Rozpoznawanie pisma odręcznego w inteligentnych systemach projektowania

• Języki publikacji: EN

Abstrakty

EN

This article presents selected research on the development of complex fundamentals of building intelligent interactive systems for design of machine elements and assemblies on the basis of its features described in a natural language. We propose a new method for handwriting recognition that utilizes geometric features of letters. The article deals with recognition of isolated handwritten characters using neural networks. As a result of the geometrical analysis, graphical representations of recognized characters are obtained in the form of pattern descriptions of isolated characters. Selected parameters of the characters are inputs to the neural network for writing recognition which is font independent. In this article, we present a new method for off-line natural writing recognition and also describe our research and conclusions on the experiments.

PL

W artykule przedstawiono wybrane prace badawcze dotyczące podstaw budowy inteligentnych systemów interakcji do projektowania elementów i zespołów maszyn na podstawie ich cech opisywanych w języku naturalnym. Zaproponowano nową metodę rozpoznawania pisma odręcznego, w której wykorzystano geometryczne cechy znaków. Artykuł dotyczy rozpoznawania izolowanych znaków pisma odręcznego za pomocą sieci neuronowych. W wyniku analizy geometrycznej otrzymuje się reprezentacje graficzne rozpoznawanych znaków w postaci opisów wzorców pojedynczych znaków. Wybrane parametry znaków stanowią wejścia sieci neuronowej do rozpoznawania pisma niezależnego od kroju. W artykule przedstawiono nową metodę rozpoznawania pisma naturalnego, a także opisano badania i podano wnioski wynikające z eksperymentów.

Słowa kluczowe

EN

human-machine interaction; intelligent automated design; intelligent system; handwriting recognition; artificial neural networks; computer-aided design

PL

interakcja człowiek-maszyna; inteligentne zautomatyzowane projektowanie; inteligentny system; rozpoznawanie pisma odręcznego; sieci neuronowe; CAD

• Wydawca: Wydawnictwo Politechniki Poznańskiej
• Czasopismo: Archives of Mechanical Technology and Automation
• Rocznik: 2014
• Tom: Vol. 34, no. 3
• Strony: 3--12
• Opis fizyczny: Bibliogr. 19 poz., rys.

Twórcy

autor

Kacalak W.

• Department of Mechanical Engineering, Koszalin University of Technology

autor

Majewski M.

• Department of Mechanical Engineering, Koszalin University of Technology

Bibliografia

• [1] Artieres T., Gauthier N., Gallinari P., Dorizzi B., A Hidden Markov Models combination framework for handwriting recognition, International Journal on Document Analysis and Recognition, 2003, vol. 5, no. 4, p. 233-243.
• [2] Besner D., Humphreys G.W., Basic Processes in Reading: Visual Word Recognition, New Jersey, Lawrence Erlbaum Associates 1991.
• [3] Bishop C.M., Neural Networks for Pattern Recognition, New York, Oxford Univ. Press 2004.
• [4] Bradford R., Bradford R.B., An Introduction to Handwriting Examination and Identification, Chicago, Nelson-Hall Publishers 1992.
• [5] Dori D., Bruckstein A., Shape, Structure and Pattern Recognition, New Jersey, World Scientific Publishing Co. 1995.
• [6] Kacalak W., Majewski M., Effective Handwriting Recognition System using Geometrical Character Analysis Algorithms, in: 19th International Conference on Neural Information Processing ICONIP2012, Doha, Qatar, 12-15 November 2012. Lecture Notes in Computer Science 7666, Part 4, Springer 2012, p. 248-255.
• [7] Kacalak W., Majewski M., New Intelligent Interactive Automated Systems for Design of Machinę Elements and Assemblies, in: 19th International Conference on Neural Information Processing ICONIP2012, Doha, Qatar, 12-15 November 2012. Lecture Notes in Computer Science 7666, Part 4, Springer 2012, p. 115-122.
• [8] Kacalak W., Majewski M., Zurada J.M., Intelligent e-leaming systems for evaluation of user's knowledge and skills with efficient information processing. Lecture Notes in Artificial Intelligence 6114, Springer 2010, p. 508-515.
• [9] Kavallieratou E., Fakotakis N., Kokkinakis G., An unconstrained handwriting recognition system, International Journal on Document Analysis and Recognition, 2002, vol. 4, no. 4, p. 226-242.
• [10] Li Z.C., Suen C.Y., The partition-combination method for recognition of handwritten characters, Pattern Recognition Letters, 2000, vol. 21, no. 8, p. 701-720.
• [11] Lippman R., An Introduction to Computing with Neural Nets, IEEE Transactions on Acoustic, Speech, and Signal Processing, IEEE Signal Processing Society, 1987, vol. 4, no. 3, p. 4-22.
• [12] Majewski M., Zurada J.M., Sentence recognition using artificial neural networks, Knowledge-Based Systems, 2008, vol. 21, issue 7, p. 629-635.
• [13] Mitiche A., Lebidoff M., Pattern Classification by a Condensed Neural Network, Neural Networks, 2001, vol. 14, no. 4-5, p. 575-580.
• [14] Mori S., Nishida H., Yamada H., Optical Character Recognition, New York, Wiley 1999.
• [15] Nishida H., An Approach to Integration of Off-Line and On-Line Recognition of Handwriting, Pattern Recognition Letters, 1995, vol. 16, no. 11, p. 1213-1219.
• [16] Parzen E., On estimation of a probability density function and mode, The Annals of Mathematical Statistics, 1962, vol. 33, no. 3, p. 1065-1076.
• [17] Specht D.F., A general regression neural network, IEEE Transactions on Neural Networks, 1991, vol. 2, no. 6, p. 568-576.
• [18] Specht D.F., Probabilistic neural networks, Neural Networks, 1990, vol. 3, no. 1, p. 109-118.
• [19] Zhou J., Krzyzak A., Suen C.Y., Verification-a Method of Enhancing the Recognizers of Isolated and Touching Handwritten Numerals, Pattern Recognition, 2002, vol. 35, no. 5, p. 1179-1189.