Full-Page Text Recognition: Learning Where to Start and When to Stop

Bastien Moysset 1, 2 Christopher Kermorvant 2 Christian Wolf 1
1 imagine - Extraction de Caractéristiques et Identification
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Abstract : Text line detection and localization is a crucial step for full page document analysis, but still suffers from heterogeneity of real life documents. In this paper, we present a new approach for full page text recognition. Localization of the text lines is based on regressions with Fully Convolutional Neural Networks and Multidimensional Long Short-Term Memory as contextual layers. In order to increase the efficiency of this localization method, only the position of the left side of the text lines are predicted. The text recognizer is then in charge of predicting the end of the text to recognize. This method has shown good results for full page text recognition on the highly heterogeneous Maurdor dataset.
Document type :
Conference papers
Complete list of metadatas

https://hal.archives-ouvertes.fr/hal-01563124
Contributor : Christian Wolf <>
Submitted on : Monday, July 17, 2017 - 12:10:52 PM
Last modification on : Tuesday, February 26, 2019 - 4:35:37 PM

Identifiers

  • HAL Id : hal-01563124, version 1

Citation

Bastien Moysset, Christopher Kermorvant, Christian Wolf. Full-Page Text Recognition: Learning Where to Start and When to Stop. International Conference on Document Analysis and Recognition, Nov 2017, Kyoto, Japan. ⟨hal-01563124⟩

Share

Metrics

Record views

246