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- W4288101093 abstract "Offline handwriting recognition with deep neural networks is usually limited to words or lines due to large computational costs. In this paper, a less computationally expensive full page offline handwritten text recognition framework is introduced. This framework includes a pipeline that locates handwritten text with an object detection neural network and recognises the text within the detected regions using features extracted with a multi-scale convolutional neural network (CNN) fed into a bidirectional long short term memory (LSTM) network. This framework achieves comparable error rates to state of the art frameworks while using less memory and time. The results in this paper demonstrate the potential of this framework and future work can investigate production ready and deployable handwritten text recognisers." @default.
- W4288101093 created "2022-07-28" @default.
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- W4288101093 date "2019-10-01" @default.
- W4288101093 modified "2023-09-27" @default.
- W4288101093 title "A Computationally Efficient Pipeline Approach to Full Page Offline Handwritten Text Recognition" @default.
- W4288101093 doi "https://doi.org/10.48550/arxiv.1910.00663" @default.
- W4288101093 hasPublicationYear "2019" @default.
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