Context-directed segmentation algorithm for handwritten numeral strings
Image and Vision Computing
Character recognition—a review
Pattern Recognition
Using Generative Models for Handwritten Digit Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Lexicon Driven Approach to Handwritten Word Recognition for Real-Time Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Representation and Recognition of Handwritten Digits Using Deformable Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
A clustering algorithm using an evolutionary programming-based approach
Pattern Recognition Letters
Postprocessing of Recognized Strings Using Nonstationary Markovian Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Matching Using LAT and its Application to Handwritten Numeral Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Character Recognition Without Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer-aided design applications of the rational b-spline approximation form.
Computer-aided design applications of the rational b-spline approximation form.
Handwritten word recognition with character and inter-character neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Clustered defect detection of high quality chips using self-supervised multilayer perceptron
Expert Systems with Applications: An International Journal
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Recognition of connected handwritten digit strings is a challenging task due mainly to two problems: poor character segmentation and unreliable isolated character recognition. In this paper, we first present a rational B-spline representation of digit templates based on Pixel-to-Boundary Distance (PBD) maps. We then present a neural network approach to extract B-spline PBD templates and an evolutionary algorithm to optimize these templates. In total, 1,000 templates (100 templates for each of 10 classes) were extracted from and optimized on 10,426 training samples from the NIST Special Database 3. By using these templates, a nearest neighbor classifier can successfully reject 90.7 percent of nondigit patterns while achieving a 96.4 percent correct classification of isolated test digits. When our classifier is applied to the recognition of 4,958 connected handwritten digit strings (4,555 2-digit, 355 3-digit, and 48 4-digit strings) from the NIST Special Database 3 with a dynamic programming approach, it has a correct classification rate of 82.4 percent with a rejection rate of as low as 0.85 percent. Our classifier compares favorably in terms of correct classification rate and robustness with other classifiers that are tested.