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Found 2 papers, 1 papers with code
A Neural Model for Text Localization, Transcription and Named Entity Recognition in Full Pages
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.
Joint Recognition of Handwritten Text and Named Entities with a Neural End-to-end Model
When extracting information from handwritten documents, text transcription and named entity recognition are usually faced as separate subsequent tasks.
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