Abstract
Various handwritten digits recognition methods have been developed and their good performances have been reported. Nonetheless, recognizing digits types and patterns remains a challenging task. Recognition performance is highly affected by image corruption, perspective and illumination variation. On the other hand, vectors and matrices are sometimes not suitable or enough to describe the intrinsic nature of data from various fields of data processing. In this paper, we propose a handwritten digit classification method based on robust higher-order tensors. The proposed approach handles invariance to occlusions, misalignment and illumination variation. Evaluation of the MNIST and USPS handwritten numeral databases is performed to assess the performance and effectiveness of the proposed method. Substantial results are achieved in terms of recognition accuracy, image alignment, numerical stability and computational speed.
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Rahmani, D., Nakouri, H. (2020). Robust Handwritten Digit Classification Using Tensors Decomposition. In: B. R., P., Thenkanidiyoor, V., Prasath, R., Vanga, O. (eds) Mining Intelligence and Knowledge Exploration. MIKE 2019. Lecture Notes in Computer Science(), vol 11987. Springer, Cham. https://doi.org/10.1007/978-3-030-66187-8_25
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DOI: https://doi.org/10.1007/978-3-030-66187-8_25
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