@InProceedings{ManuelCarbonell2019,
author="Manuel Carbonell
and Joan Mas
and Mauricio Villegas
and Alicia Fornes
and Josep Llados",
title="End-to-End Handwritten Text Detection and Transcription in Full Pages",
booktitle="2nd International Workshop on Machine Learning",
year="2019",
volume="5",
pages="29--34",
optkeywords="Handwritten Text Recognition",
optkeywords="Layout Analysis",
optkeywords="Text segmentation",
optkeywords="Deep Neural Networks",
optkeywords="Multi-task learning",
abstract="When transcribing handwritten document images, inaccuracies in the text segmentation step often cause errors in the subsequent transcription step. For this reason, some recent methods propose to perform the recognition at paragraph level. But still, errors in the segmentation of paragraphs can affectthe transcription performance. In this work, we propose an end-to-end framework to transcribe full pages. The joint text detection and transcription allows to remove the layout analysis requirement at test time. The experimental results show that our approach can achieve comparable results to models that assumesegmented paragraphs, and suggest that joining the two tasks brings an improvement over doing the two tasks separately.",
optnote="DAG; 600.140; 601.311; 600.140",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=3353), last updated on Thu, 11 Mar 2021 23:12:58 +0100",
doi="10.1109/ICDARW.2019.40077",
opturl="https://ieeexplore.ieee.org/document/8892971",
file=":http://refbase.cvc.uab.es/files/CMV2019.pdf:PDF"
}