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metadata
dataset_info:
  features:
    - name: input_ids
      sequence: int32
    - name: bbox
      dtype:
        array2_d:
          shape:
            - 512
            - 4
          dtype: int32
    - name: attention_mask
      sequence: int32
    - name: image
      dtype:
        array3_d:
          shape:
            - 3
            - 224
            - 224
          dtype: int32
    - name: start_positions
      dtype: int32
    - name: end_positions
      dtype: int32
    - name: questions
      dtype: string
    - name: answers
      dtype: string
  splits:
    - name: train
      num_bytes: 89021492745
      num_examples: 143765
  download_size: 913954164
  dataset_size: 89021492745
license: mit
language:
  - cs
tags:
  - finance

CIVQA EasyOCR LayoutLM Train 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 train split. The validation part of the dataset can be found on this URL: https://huggingface.co/datasets/fimu-docproc-research/CIVQA_EasyOCR_LayoutLM_Validation
The pre-encoded train dataset can be found on this link: https://huggingface.co/datasets/fimu-docproc-research/CIVQA_EasyOCR_Train

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.