TY  - JOUR
AU  - Manuel Carbonell
AU  - Alicia Fornes
AU  - Mauricio Villegas
AU  - Josep Llados
PY  - 2020//
TI  - A Neural Model for Text Localization, Transcription and Named Entity Recognition in Full Pages
T2  - PRL
JO  - Pattern Recognition Letters
SP  - 219
EP  - 227
VL  - 136
N2  - In the last years, the consolidation of deep neural network architectures for information extraction in document images has brought big improvements in the performance of each of the tasks involved in this process, consisting of text localization, transcription, and named entity recognition. However, this process is traditionally performed with separate methods for each task. In this work we propose an end-to-end model that combines a one stage object detection network with branches for the recognition of text and named entities respectively in a way that shared features can be learned simultaneously from the training error of each of the tasks. By doing so the model jointly performs handwritten text detection, transcription, and named entity recognition at page level with a single feed forward step. We exhaustively evaluate our approach on different datasets, discussing its advantages and limitations compared to sequential approaches. The results show that the model is capable of benefiting from shared features by simultaneously solving interdependent tasks.
L1  - http://refbase.cvc.uab.es/files/CFV2020.pdf
N1  - DAG; 600.140; 601.311; 600.121
ID  - Manuel Carbonell2020
ER  -