Abstract
Modern OCRs rely on the use of recurrent deep neural networks for their text recognition task. Despite its known capacity to achieve high accuracies, it is shown that these networks suffer from several drawbacks. Mainly, they are highly dependent on the training data-set and particularly non-resistant to shift and position variability. A data augmentation policy is proposed to remedy this problem. This policy allows generating realistic variations from the original data. A novel fast and efficient fully convolutional neural network (FCNN) with invariance properties is also proposed. Its structure is mainly based on a multi-resolution pyramid of dilated convolutions. It can be seen as an end-to-end 2D signal to sequence analyzer that does not need recurrent layers. In this work, extensive experiments have been held to study the stability of recurrent networks. It is shown that data augmentation significantly improves network stability. Experiments have also confirmed the advantage of the PyraD-DCNN based system in terms of performance but also in terms of stability and resilience to position and shift variability. A private data-set composed of more than 600k images has been used in this work.
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Awal, AM., Neitthoffer, T., Ghanmi, N. (2021). Data Augmentation vs. PyraD-DCNN: A Fast, Light, and Shift Invariant FCNN for Text Recognition. In: Barney Smith, E.H., Pal, U. (eds) Document Analysis and Recognition – ICDAR 2021 Workshops. ICDAR 2021. Lecture Notes in Computer Science(), vol 12917. Springer, Cham. https://doi.org/10.1007/978-3-030-86159-9_3
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DOI: https://doi.org/10.1007/978-3-030-86159-9_3
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