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 orientationand 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.
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 Image to Sequence architecture, it can extract text present in an image and then sequence it correctly without imposing any constraints regarding orientation, layout and size of text and non-text. Further, it can also be trained to generate auxiliary markup related to formatting, layout and content. We use character level vocabulary, thereby enabling language and terminology of any subject. The model achieves a new state-of-art in paragraph level recognition on the IAM dataset . When evaluated on scans of real world handwritten free form test answers - beset with curved and slanted lines, drawings, tables, math, chemistry and other symbols - it performs better than all commercially available HTR cloud APIs. It is deployed in production as part of a commercial web application.
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