Deformation-Invariant Networks For Handwritten Text Recognition

Image deformations under simple geometric restrictions are crucial for Handwriting Text Recognition (HTR), since different writing styles can be viewed as simple geometrical deformations of the same textual elements. In this respect, the usefulness of including deformation invariance to an HTR system is indisputable. We explore different existing strategies for ensuring deformation invariance, including spatial transformers and deformable convolutions, under the context of text recognition, as well as introduce a new deformation-based algorithm, inspired by adversarial learning, which aims to reduce character output uncertainty during evaluation time. The resulting HTR system is shown to achieve state-of-the-art performance on the IAM and RIMES datasets.