Challenges in End-to-End Neural Scientific Table Recognition
| Autor: | Yuntian Deng, Gideon Mann, David Rosenberg |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Computer science
business.industry Pattern recognition 02 engineering and technology Optical character recognition 010501 environmental sciences computer.software_genre 01 natural sciences Digital image ComputingMethodologies_PATTERNRECOGNITION End-to-end principle 0202 electrical engineering electronic engineering information engineering Table (database) 020201 artificial intelligence & image processing Artificial intelligence business computer 0105 earth and related environmental sciences |
| Zdroj: | ICDAR |
| Popis: | In recent years, end-to-end trained neural models have been applied successfully to various optical character recognition (OCR) tasks. However, the same level of success has not yet been achieved in end-to-end neural scientific table recognition, which involves multi-row/multi-column structures and math formulas in cells. In this paper, we take a step forward to full end-to-end scientific table recognition by constructing a large dataset consisting of 450K table images paired with corresponding LaTeX sources. We apply a popular attentional encoder-decoder model to this dataset and show the potential of end-to-end trained neural systems, as well as associated challenges. |
| Databáze: | OpenAIRE |
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