Structured document understanding has attracted considerable attention and made significant progress recently, owing to its crucial role in intelligent document processing. However, most existing related models can only deal with the document data of specific language(s) (typically English) included in thepre-training collection, which is extremely limited. To address this issue, we propose a simple yet effective Language-independent Layout Transformer (LiLT) for structured document understanding. LiLT can be pretrained on the structured documents of a single language and then directly fine-tuned onother languages with the corresponding offthe-shelf monolingual/multilingual pre-trained textual models. Experimental results on eight languages have shown that LiLT can achieve competitive or even superior performance on diverse widely-used downstream benchmarks,which enables language-independent benefit from the pre-training of document layout structure.
Video explains the Fine-tuning of the LiLT model to extract information from documents like Invoices, Receipts, Financial Documents, tables, etc.
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1.Notebook:github.com/karndeepsingh/Extr...
2. LiLT Paper: arxiv.org/pdf/2202.13669.pdf
3. FUNSD Dataset: guillaumejaume.github.io/FUNSD/
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10 дек 2022
