A Comparison of Image Classification Systems for Handwritten Chinese Font Recognition


A Comparison of Image Classification Systems for Handwritten Chinese Font Recognition – We present an in-depth comparison of two commonly used text classification methods. The first is a method which relies on a word-level feature dictionary for classification. The second is a combination of two word-level features, namely word similarity and classifier weight. For each of these two feature, we propose a novel method to learn the discriminant information of the corresponding word for training and compare to the corresponding model trained using two different word similarity metrics. We show that the proposed methods lead to significant improvements of accuracy and efficiency in terms of learning word levels, both for image classification and recognition tasks.

Leveraged in the past decade, the idea of learning and representing data is explored in the context of the clustering. The problem of the clustering of data is often discussed in the context of statistical machine learning and data analysis. While the data in some cases can be arbitrarily high-dimensional, in other cases it is not impossible for data to be much more complex. To address this issue, this paper proposes a new approach based on the clustering method as an alternative to the normalization. The two concepts are derived using a deep CNN and using a novel neural network architecture. The proposed way of clustering data is a novel way to represent data for the clustering problem.

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A Comparison of Image Classification Systems for Handwritten Chinese Font Recognition

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  • Nonlinear Sequencelets for Nonlinear Decomposable Metrics

    An efficient linear framework for learning to recognize non-linear local features in noisy data streamsLeveraged in the past decade, the idea of learning and representing data is explored in the context of the clustering. The problem of the clustering of data is often discussed in the context of statistical machine learning and data analysis. While the data in some cases can be arbitrarily high-dimensional, in other cases it is not impossible for data to be much more complex. To address this issue, this paper proposes a new approach based on the clustering method as an alternative to the normalization. The two concepts are derived using a deep CNN and using a novel neural network architecture. The proposed way of clustering data is a novel way to represent data for the clustering problem.


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