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Handwriting recognition in intelligent design systems

Kacalak W., Majewski M.

Archives of Mechanical Technology and Automation

2014

Vol. 34, no. 3

3--12

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.

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.

Exploring the Human Handwriting Process

Bouslama F., Benrejeb M.

International Journal of Applied Mathematics and Computer Science

2000

Vol. 10, no. 4

877-904

The study of human handwriting movements is of great interest to researchers and biologists. It can lead to an understanding of the properties of the biological system that generates the human handwriting movements. With the identification of a dynamical system that exhibits characteristics similar to the biological one, it is possible to study handwriting movements, to identify their driving motor signals and to try to reproduce by machine the handwriting motion. In this paper, we give an analysis of modeling techniques for the handwriting process proposed in the literature. We show that either mathematical models based on the trajectories of handwritten objects or physical models obtained from the dynamic motion of the human hand can be used for modeling the handwriting process. We study the handwriting movements using these approaches and we give an analysis and synthesis of the driving motor signals. We apply the results to the generation of handwritten Arabic letters. We then propose a new synthesis technique where a more promising and realistic model can be obtained. We present the basic idea and expect that some improvement over the previous techniques can be obtained. The technique is based on Feedback Error Learning neural networks.