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metadata
dataset_info:
  features:
    - name: input_ids
      sequence: int64
    - name: bbox
      dtype:
        array2_d:
          shape:
            - 512
            - 4
          dtype: int64
    - name: attention_mask
      sequence: int64
    - name: image
      dtype:
        array3_d:
          shape:
            - 3
            - 224
            - 224
          dtype: int64
    - name: start_positions
      dtype: int64
    - name: end_positions
      dtype: int64
    - name: questions
      dtype: string
    - name: answers
      dtype: string
  splits:
    - name: validation
      num_bytes: 21069068623
      num_examples: 17079
  download_size: 707118847
  dataset_size: 21069068623
license: mit
language:
  - cs
tags:
  - finance

CIVQA EasyOCR LayoutLM Validation Dataset

The CIVQA (Czech Invoice Visual Question Answering) dataset was created with EasyOCR, and it is encoded for LayoutLM models. This dataset contains only the validation split. The train part of the dataset can be found on this URL: https://huggingface.co/datasets/fimu-docproc-research/CIVQA_EasyOCR_LayoutLM_Train
The pre-encoded validation dataset can be found on this link: https://huggingface.co/datasets/fimu-docproc-research/CIVQA_EasyOCR_Validation

All invoices used in this dataset were obtained from public sources. Over these invoices, we were focusing on 15 different entities, which are crucial for processing the invoices.

  • Invoice number
  • Variable symbol
  • Specific symbol
  • Constant symbol
  • Bank code
  • Account number
  • ICO
  • Total amount
  • Invoice date
  • Due date
  • Name of supplier
  • IBAN
  • DIC
  • QR code
  • Supplier's address

The invoices included in this dataset were gathered from the internet. We understand that privacy is of utmost importance. Therefore, we sincerely apologise for any inconvenience caused by including your identifiable information in this dataset. If you have identified your data in this dataset and wish to have it removed from research purposes, we request you kindly to access the following URL: https://forms.gle/tUVJKoB22oeTncUD6

We profoundly appreciate your cooperation and understanding in this matter.