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A Sequential Handwriting Recognition Model Based on a Dynamically Configurable CRNN
2021
Sensors
Handwriting recognition refers to recognizing a handwritten input that includes character(s) or digit(s) based on an image. Because most applications of handwriting recognition in real life contain sequential text in various languages, there is a need to develop a dynamic handwriting recognition system. Inspired by the neuroevolutionary technique, this paper proposes a Dynamically Configurable Convolutional Recurrent Neural Network (DC-CRNN) for the handwriting recognition sequence modeling
doi:10.3390/s21217306
pmid:34770612
pmcid:PMC8587523
fatcat:o2umjghgcvccrb7g3tonz4nf34