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Several convolutional neural network architectures have been proposed for handwritten character recognition. However, most of the conventional architectures demand large scale training data and long training time to obtain satisfactory results. These requirements prevent the use of these methods in a broader range of applications. As an alternative to cope with these problems, we present a new convolutional network for handwritten character recognition based on the Fukunaga–Koontz transform (FKT). Our approach lies in the assumption that Fukunaga–Koontz convolutional kernels can be efficiently learned from subspaces and directly employed to produce high discriminant features in a shallow network architecture. When representing image classes by subspaces, the within-class separability is reduced, since the subspaces form clusters in a low-dimensional space. To increase the between-class separability, we compute a discriminative space from the training subspaces using FKT. By learning convolutional kernels from subspaces, it is possible to extract representative and discriminative features from an image with only a few parameters. Another contribution of the proposed network is the use of pooling layers, which further improves its performance. The proposed method, called Fukunaga–Koontz Network (FKNet), is suitable for solving practical problems, especially when training and processing times are constraints. Four publicly available handwritten character datasets are employed to evaluate the advantages of FKNet. In addition, we demonstrate the flexibility of the proposed method by experiments on LFW dataset.

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This work presents a shallow network based on subspaces with applications in image classification. Recently, shallow networks based on PCA filter banks have been employed to solve many computer vision-related problems including texture classification, face recognition, and scene understanding. These approaches are robust, with a straightforward implementation that enables fast prototyping of practical applications. However, these architectures employ either unsupervised or supervised learning. As a result, they may not achieve highly discriminative features in more complicated computer vision problems containing variations in camera motion, object's appearance, pose, scale, and texture, due to drawbacks related to each learning paradigm. To cope with this disadvantage, we propose a semi-supervised shallow network equipped with both unsupervised and supervised filter banks, presenting representative and discriminative abilities. Besides, the introduced architecture is flexible, performing favorably on different applications whose amount of supervised data is an issue, making it an attractive choice in practice. The proposed network is evaluated on five datasets. The results show improvement in terms of prediction rate, comparing to current shallow networks.