  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.
