A Computationally Efficient Pipeline Approach to Full Page Offline Handwritten Text Recognition
This addresses the problem of computational efficiency for researchers and practitioners in document analysis and handwriting recognition, though it appears incremental as it builds on existing detection and recognition components.
The paper tackles the computational cost limitation of full-page offline handwritten text recognition by introducing a pipeline framework that uses object detection and multi-scale CNN features with bidirectional LSTM, achieving comparable error rates to state-of-the-art methods while using less memory and time.
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.