| Autor: |
Arsen Yeghiazaryan, Khachatur Khechoyan, Grigor Nalbandyan, Sipan Muradyan |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
IEEE Access, Vol 10, Pp 39261-39268 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2022.3164674 |
| Popis: |
Key information extraction from unstructured documents is a practical problem in many industries. Machine learning models aimed at solving this problem should efficiently utilize textual, visual, and 2D spatial layout information of the document. Grid based approaches achieve this by representing the document as a 2D grid and feeding it to a fully convolutional encoder-decoder network that solves a semantic instance segmentation problem. We propose a new method for the instance detection branch of that network for the task of automatic information extraction from invoices. Our approach reduces this problem to 1D region detection. The proposed network has fewer parameters and a shorter inference times. Additionally, we suggest a new metric for evaluating the results. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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