Towards Writing Style Adaptation in Handwriting Recognition

Understand

One of the challenges of handwriting recognition is to transcribe a large number of vastly different writing styles.

• State-of-the-art approaches do not explicitly use information about the writer's style, which may be limiting overall accuracy due to various ambiguities.
• We explore models with writer-dependent parameters which take the writer's identity as an additional input.
• The proposed models can be trained on datasets with partitions likely written by a single author (e.g.