  Recent advances in handwritten text recognition enabled to recognize whole
documents in an end-to-end way: the Document Attention Network (DAN) recognizes
the characters one after the other through an attention-based prediction
process until reaching the end of the document. However, this autoregressive
process leads to inference that cannot benefit from any parallelization
optimization. In this paper, we propose Faster DAN, a two-step strategy to
speed up the recognition process at prediction time: the model predicts the
first character of each text line in the document, and then completes all the
text lines in parallel through multi-target queries and a specific document
positional encoding scheme. Faster DAN reaches competitive results compared to
standard DAN, while being at least 4 times faster on whole single-page and
double-page images of the RIMES 2009, READ 2016 and MAURDOR datasets. Source
code and trained model weights are available at
https://github.com/FactoDeepLearning/FasterDAN.
