Comments (1)
Hi, thank you for your interest on our work :)
For (1), I hope the following links would be helpful,
- https://github.com/clovaai/donut#for-document-information-extraction
- https://arxiv.org/abs/2111.15664
- especially, Section 2.4 and Appendix A.4 and Figure E.
For (2), I would like to say that the SynthDoG's purpose is not to create synthetic data resembling actual forms/invoices. The purpose is just to create a simple synthetic document with texts. The link below seems to be helpful to you.
- https://github.com/tuanpham-dev/react-invoice-generator
- https://github.com/amoffat/metabrite-receipt-tests
Hope this helps. Please let me know if you are still confused.
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Related Issues (20)
- DOCVQA data set format ? HOT 3
- Prediction and Answer differ by dataset-specific tag HOT 1
- The latest update has the model weights twice the embedding dim size of the actual model installed through github or pip HOT 2
- VisionEncoderDecoderModel convert HOT 2
- Integrate a customized internal OCR engine to Donut HOT 1
- Request: Dataset and pretrained model for language detection
- Problem with getting predictions HOT 7
- Does synthdog data has MiT or afl-3.0 license? HOT 1
- Error "A configuraton of type donut cannot be instantiated because not both `encoder` and `decoder` sub-configurations are passed" when run inference after finetuned docvqa without pushing to hugging face? HOT 1
- custom json schema - ASAP HOT 2
- Multi GPU support for fine tuning
- How to extract complete text from the document? HOT 3
- confidence 값의 공식적인 지원
- Classification inference
- Update donut-python Python Package to be compatible with latest versions of transformers
- donut inference시 sub task가 변경?
- Not getting prediction correctly using the model trained on the custom dataset (similar format as CORD-V2 dataset) HOT 6
- not work this app.py
- Can synthdog insert text for a specified bbox? HOT 1
- Where is the fine-tuned model?
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