  We present a Neural Network based Handwritten Text Recognition (HTR) model
architecture that can be trained to recognize full pages of handwritten or
printed text without image segmentation. Being based on an Image to Sequence
architecture, it can be trained to extract text present in an image and
sequence it correctly without imposing any constraints on language, shape of
characters or orientation and layout of text and non-text. The model can also
be trained to generate auxiliary markup related to formatting, layout and
content. We use character level token vocabulary, thereby supporting proper
nouns and terminology of any subject. The model achieves a new state-of-art in
full page recognition on the IAM dataset and when evaluated on scans of real
world handwritten free form test answers - a dataset beset with curved and
slanted lines, drawings, tables, math, chemistry and other symbols - it
performs better than all commercially available HTR APIs. It is deployed in
production as part of a commercial web application.
