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Author Manuel Carbonell; Alicia Fornes; Mauricio Villegas; Josep Llados edit   pdf
  Title A Neural Model for Text Localization, Transcription and Named Entity Recognition in Full Pages Type Journal Article
  Year 2020 Publication (up) Pattern Recognition Letters Abbreviated Journal PRL  
  Volume 136 Issue Pages 219-227  
  Abstract 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.  
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  Area Expedition Conference  
  Notes DAG; 600.140; 601.311; 600.121 Approved no  
  Call Number Admin @ si @ CFV2020 Serial 3451  
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