Abstract
Offline handwriting recognition requires a combination of computer vision and sequence learning. In most systems the two elements are handled separately, with sophisticated pre-processing techniques used to extract the image features and sequential models such as HMMs used to provide the transcriptions. This chapter considers an alternative system, based on multidimensional recurrent neural networks, that learns directly from pixel data, and describes its winning entry to a major Arabic offline handwriting recognition competition.
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Graves, A. (2012). Offline Arabic Handwriting Recognition with Multidimensional Recurrent Neural Networks. In: Märgner, V., El Abed, H. (eds) Guide to OCR for Arabic Scripts. Springer, London. https://doi.org/10.1007/978-1-4471-4072-6_12
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DOI: https://doi.org/10.1007/978-1-4471-4072-6_12
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