PT Unknown
AU Manuel Carbonell
   Joan Mas
   Mauricio Villegas
   Alicia Fornes
   Josep Llados
TI End-to-End Handwritten Text Detection and Transcription in Full Pages
BT 2nd International Workshop on Machine Learning
PY 2019
BP 29
EP 34
VL 5
DI 10.1109/ICDARW.2019.40077
DE Handwritten Text Recognition; Layout Analysis; Text segmentation; Deep Neural Networks; Multi-task learning
AB 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.
ER