SPAN: a Simple Predict & Align Network for Handwritten Paragraph Recognition

TL;DR

The paper introduces SPAN, an end-to-end, segmentation-free neural network for recognizing handwritten paragraphs, achieving competitive accuracy without prior line segmentation or dataset adaptation.

Contribution

It presents a simple, recurrence-free, fully convolutional model that recognizes handwritten paragraphs directly at the document level, eliminating the need for segmentation and line break annotations.

Findings

Achieves competitive results on RIMES, IAM, and READ 2016 datasets.

Does not require segmentation labels or line break annotations.

Can be trained from scratch without dataset adaptation.

Abstract

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.