  Unconstrained handwriting recognition is an essential task in document
analysis. It is usually carried out in two steps. First, the document is
segmented into text lines. Second, an Optical Character Recognition model is
applied on these line images. We propose the Simple Predict & Align Network: an
end-to-end recurrence-free Fully Convolutional Network performing OCR at
paragraph level without any prior segmentation stage. The framework is as
simple as the one used for the recognition of isolated lines and we achieve
competitive results on three popular datasets: RIMES, IAM and READ 2016. The
proposed model does not require any dataset adaptation, it can be trained from
scratch, without segmentation labels, and it does not require line breaks in
the transcription labels. Our code and trained model weights are available at
https://github.com/FactoDeepLearning/SPAN.
