Resolving multifont character confusion with neural networks

Wang, Jin; Jean, Jack

doi:10.1016/0031-3203(93)90099-i

Cited by 32 publications

(12 citation statements)

References 13 publications

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“…Teo and Shinghal (1997) combine a ruled-based classifier with PNN to confirm or reject data. OCR applications are also presented by Avi-Itzhak et al (1995) and Wang and Jean (1993) to solve multifont character recognition and confusions with hierarchical NN. In our approach, a model-based classifier find the most relevant pair of classes.…”

Section: Model Generation Vs Discriminationmentioning

confidence: 99%

Hybrid generative/discriminative classifier for unconstrained character recognition

Prévost

Oudot

Moises

et al. 2005

Pattern Recognition Letters

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Section: Model Generation Vs Discriminationmentioning

confidence: 99%

Hybrid generative/discriminative classifier for unconstrained character recognition

Prévost

Oudot

Moises

et al. 2005

Pattern Recognition Letters

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“…Dynamic sampling techniques have become an interesting way to deal with the class imbalance problem on the Multilayer Perceptron (MLP) trained with stochastic back-propagation [19,27,28,31,32]. Different from conventional strategies as over-and/or under-sampling techniques, the dynamic sampling finds automatically in the training stage the properly sampling amount for each class for dealing with the class imbalance problem.…”

Section: Dynamic Sampling Techniques To Train Artificial Neural Networkmentioning

confidence: 99%

“…The main difference of these methods with respect to the conventional sampling strategies is in the time when they sample the data or when they select the examples to be sampled (see [19,27,28,31,32]). …”

Section: Introductionmentioning

confidence: 99%

A Selective Dynamic Sampling Back-Propagation Approach for Handling the Two-Class Imbalance Problem

Alejo¹,

Monroy-de-Jesús

Pacheco-Sánchez

et al. 2016

Applied Sciences

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Abstract:In this work, we developed a Selective Dynamic Sampling Approach (SDSA) to deal with the class imbalance problem. It is based on the idea of using only the most appropriate samples during the neural network training stage. The "average samples"are the best to train the neural network, they are neither hard, nor easy to learn, and they could improve the classifier performance. The experimental results show that the proposed method is a successful method to deal with the two-class imbalance problem. It is very competitive with respect to well-known over-sampling approaches and dynamic sampling approaches, even often outperforming the under-sampling and standard back-propagation methods. SDSA is a very simple method for automatically selecting the most appropriate samples (average samples) during the training of the back-propagation, and it is very efficient. In the training stage, SDSA uses significantly fewer samples than the popular over-sampling approaches and even than the standard back-propagation trained with the original dataset.

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“…On the other hand, the recognition is very difficult when different letters have similar features: for example the letter 'l' in one font could be very similar to the digit '1' in another font. There are several methods to get over this kind of problem, such as the use of a context dependent post-processing to distinguish between letters and digits [1] [2] or the use of an Optical Font Recognizer (OFR), to detect the font type and subsequently convert the multifont problem into mono-font character recognition. An OFR can be useful also to simply characterize single characters, words or paragraphs in a printed document, as an aid to analysis of document characteristics and layout.…”

Section: Introductionmentioning

confidence: 99%