Abstract
We present our current work on building a deep learning architecture for the offline handwritten character recognition problem. The proposed system is based on training a deep Convolutional Neural Network (CNN) to recognize handwritten characters, using a new synthetic character database derived from UNIPEN dataset. The presented approach is inspired in some successfully-used neural architectures for image classification, specially the VGG-CNN. Our system reads each word with the help of a sliding window in a similar way to how humans do. An innovative feature of our proposal is using a synthetic character database specifically built, in a optimized way, to identify the characters as component elements of the words. Experiments with this new training synthetic dataset produced recognition rates of 98.4% for uppercase and 96.3% for lowercase, respectively.
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Acknowledgements
This work was funded by the Spanish Ministry of Economy and Competitiveness project number TIN2014-57458-R and by the URJC-Banco de Santander Excellence Research Groups grant number 30VCPIGI09.
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Sueiras, J., Ruiz, V., Sánchez, Á., Vélez, J.F. (2017). Using a Synthetic Character Database for Training Deep Learning Models Applied to Offline Handwritten Recognition. In: Madureira, A., Abraham, A., Gamboa, D., Novais, P. (eds) Intelligent Systems Design and Applications. ISDA 2016. Advances in Intelligent Systems and Computing, vol 557. Springer, Cham. https://doi.org/10.1007/978-3-319-53480-0_30
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