HANDWRITTEN CHARACTER RECOGNITION USING NONSYMMETRICAL PERCEPTUAL ZONING

2007 ◽  
Vol 21 (01) ◽  
pp. 135-155 ◽  
Author(s):  
CINTHIA O. A. FREITAS ◽  
LUIZ S. OLIVEIRA ◽  
FLÁVIO BORTOLOZZI ◽  
SIMONE B. K. AIRES
Keyword(s):  
Character Recognition ◽  
Handwriting Recognition ◽  
Input Image ◽  
Alternative Strategy ◽  
Handwritten Character Recognition ◽  
Handwritten Character ◽  
Recognition Systems ◽  
Confusion Matrices

In this paper we present an alternative strategy to define zoning for handwriting recognition, which is based on nonsymmetrical perceptual zoning. The idea is to extract some knowledge from the confusion matrices in order to make the zoning process less empirical. The feature set considered in this work is based on concavities/convexities deficiencies, which are obtained by labeling the background pixels of the input image. To better assess the nonsymmetrical zoning we carried out experiments using four different zonings strategies. Experiments show that the nonsymmetrical zoning could be considered as a tool to build more reliable handwriting recognition systems.

Elastic Matching Techniques for Handwritten Character Recognition

2008 ◽  
pp. 17-38
Author(s):  
Seiichi Uchida
Keyword(s):  
Statistical Analysis ◽  
Character Recognition ◽  
Practical Importance ◽  
Handwritten Character Recognition ◽  
Work Related ◽  
Handwritten Character ◽  
Recognition Systems ◽  
Matching Models ◽  
Future Work ◽  
Matching Techniques

This chapter reviews various elastic matching techniques for handwritten character recognition. Elastic matching is formulated as an optimization problem of planar matching, or pixel-to-pixel correspondence, between two character images under a certain matching model, such as affine and nonlinear. Use of elastic matching instead of rigid matching improves the robustness of recognition systems against geometric deformations in handwritten character images. In addition, the optimized matching represents the deformation of handwritten characters and thus is useful for statistical analysis of the deformation. This chapter argues the general property of elastic matching techniques and their classification by matching models and optimization strategies. It also argues various topics and future work related to elastic matching for emphasizing theoretical and practical importance of elastic matching.

An Efficient Handwritten Character Recognition Using Quantum Multilayer Neural Network (QMLNN) Architecture

2021 ◽  
pp. 435-446
Author(s):  
Debanjan Konar ◽  
Suman Kalyan Kar
Keyword(s):  
Neural Network ◽  
Character Recognition ◽  
Input Image ◽  
Handwritten Character Recognition ◽  
Handwritten Character ◽  
Fuzzy Input ◽  
Feed Forward Network ◽  
Network Output ◽  
Fuzziness Measure ◽  
Neighborhood Topology

This chapter proposes a quantum multi-layer neural network (QMLNN) architecture suitable for handwritten character recognition in real time, assisted by quantum backpropagation of errors calculated from the quantum-inspired fuzziness measure of network output states. It is composed of three second-order neighborhood-topology-based inter-connected layers of neurons represented by qubits known as input, hidden, and output layers. The QMLNN architecture is a feed forward network with standard quantum backpropagation algorithm for the adjustment of its weighted interconnection. QMLNN self-organizes the quantum fuzzy input image information by means of the quantum backpropagating errors at the intermediate and output layers of the architecture. The interconnection weights are described using rotation gates. After the network is stabilized, a quantum observation at the output layer destroys the superposition of quantum states in order to obtain true binary outputs.

An Efficient Handwritten Character Recognition Using Quantum Multilayer Neural Network (QMLNN) Architecture

2018 ◽  
pp. 262-276 ◽  
Author(s):  
Debanjan Konar ◽  
Suman Kalyan Kar
Keyword(s):  
Neural Network ◽  
Character Recognition ◽  
Input Image ◽  
Handwritten Character Recognition ◽  
Handwritten Character ◽  
Fuzzy Input ◽  
Feed Forward Network ◽  
Network Output ◽  
Fuzziness Measure ◽  
Neighborhood Topology

This chapter proposes a quantum multi-layer neural network (QMLNN) architecture suitable for handwritten character recognition in real time, assisted by quantum backpropagation of errors calculated from the quantum-inspired fuzziness measure of network output states. It is composed of three second-order neighborhood-topology-based inter-connected layers of neurons represented by qubits known as input, hidden, and output layers. The QMLNN architecture is a feed forward network with standard quantum backpropagation algorithm for the adjustment of its weighted interconnection. QMLNN self-organizes the quantum fuzzy input image information by means of the quantum backpropagating errors at the intermediate and output layers of the architecture. The interconnection weights are described using rotation gates. After the network is stabilized, a quantum observation at the output layer destroys the superposition of quantum states in order to obtain true binary outputs.

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