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This paper presents a fully convolutional neural network (FCN) architecture for intelligent character recognition (ICR) that can output arbitrary length symbol streams from handwritten text. The proposed method includes a preprocessing step to normalize input blocks and introduces a probabilistic character error rate for correcting word blocks, achieving state-of-the-art results on both lexicon-based and arbitrary symbol handwriting recognition benchmarks. The research highlights the advantages of FCNs over

Author

Niva Das

Language

EN