2,795 research outputs found
On the application of reservoir computing networks for noisy image recognition
Reservoir Computing Networks (RCNs) are a special type of single layer recurrent neural networks, in which the input and the recurrent connections are randomly generated and only the output weights are trained. Besides the ability to process temporal information, the key points of RCN are easy training and robustness against noise. Recently, we introduced a simple strategy to tune the parameters of RCNs. Evaluation in the domain of noise robust speech recognition proved that this method was effective. The aim of this work is to extend that study to the field of image processing, by showing that the proposed parameter tuning procedure is equally valid in the field of image processing and conforming that RCNs are apt at temporal modeling and are robust with respect to noise. In particular, we investigate the potential of RCNs in achieving competitive performance on the well-known MNIST dataset by following the aforementioned parameter optimizing strategy. Moreover, we achieve good noise robust recognition by utilizing such a network to denoise images and supplying them to a recognizer that is solely trained on clean images. The experiments demonstrate that the proposed RCN-based handwritten digit recognizer achieves an error rate of 0.81 percent on the clean test data of the MNIST benchmark and that the proposed RCN-based denoiser can effectively reduce the error rate on the various types of noise. (c) 2017 Elsevier B.V. All rights reserved
TextCaps : Handwritten Character Recognition with Very Small Datasets
Many localized languages struggle to reap the benefits of recent advancements
in character recognition systems due to the lack of substantial amount of
labeled training data. This is due to the difficulty in generating large
amounts of labeled data for such languages and inability of deep learning
techniques to properly learn from small number of training samples. We solve
this problem by introducing a technique of generating new training samples from
the existing samples, with realistic augmentations which reflect actual
variations that are present in human hand writing, by adding random controlled
noise to their corresponding instantiation parameters. Our results with a mere
200 training samples per class surpass existing character recognition results
in the EMNIST-letter dataset while achieving the existing results in the three
datasets: EMNIST-balanced, EMNIST-digits, and MNIST. We also develop a strategy
to effectively use a combination of loss functions to improve reconstructions.
Our system is useful in character recognition for localized languages that lack
much labeled training data and even in other related more general contexts such
as object recognition
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