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--- |
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license: cc-by-4.0 |
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pretty_name: 'Brazilian Bills and Invoices Dataset' |
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language: |
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- pt |
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tags: |
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- brazil |
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- documents |
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- bills |
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- invoices |
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- receipts |
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- ocr |
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- text-recognition |
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- document-understanding |
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- ai-research |
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- computer-vision |
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- finance |
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task_categories: |
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- text-recognition |
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- document-classification |
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- document-understanding |
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size_categories: |
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- 1K<n<10K |
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--- |
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# Brazilian Bills and Invoices Dataset |
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*This dataset contains high-quality scanned and photographed images of Brazilian bills, invoices, and utility payment documents. It supports AI research in OCR, financial document understanding, and structured data extraction for Portuguese-language financial contexts.* |
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## Contact |
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For queries or collaborations related to this dataset, contact: |
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- [email protected] |
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- [email protected] |
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## Supported Tasks |
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- **Task Categories**: |
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- Text Recognition (OCR) |
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- Document Classification |
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- Document Understanding |
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- **Supported Tasks**: |
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- Extraction of key financial fields (amounts, due dates, customer IDs, payment codes) |
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- OCR for printed and digital Brazilian utility bills and invoices |
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- Document classification by service type (electricity, water, telecom, internet) |
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- Multilingual text recognition (Portuguese-English) for multinational billing systems |
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- Training AI models for financial data parsing and automation workflows |
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## Languages |
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- **Primary Language**: Portuguese |
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- **Secondary Presence**: English (on international service invoices or bilingual corporate bills) |
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## Dataset Creation |
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### Curation Rationale |
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The dataset was created to help train AI systems capable of interpreting and digitizing Brazilian billing and invoicing formats. It aids automation in finance, accounting, and document intelligence applications. |
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### Source Data |
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- **Contributors**: Collected from anonymized, publicly shared, and simulated invoice data sources |
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- **Collection Process**: Bills were photographed or scanned from utility and corporate service providers. All personal data (names, addresses, payment info) was removed or anonymized prior to inclusion. |
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### Other Known Limitations |
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- **Bias**: Major urban and corporate service providers are overrepresented |
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- **Layout Diversity**: Variations in design between companies and sectors may impact OCR performance |
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- **Image Quality**: Folded, faded, or low-resolution documents may affect data extraction accuracy |
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## Intended Uses |
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### ✅ Direct Use |
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- Training OCR and document parsing models |
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- Research in financial automation and structured document understanding |
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- Extraction of invoice-level metadata for AI accounting systems |
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- Benchmarking for multilingual document understanding tasks |
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### ❌ Out-of-Scope Use |
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- Reconstruction of individual financial histories |
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- Misuse of data for identity tracking or commercial exploitation |
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- Reproduction of proprietary billing templates for commercial gain |
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## License |
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CC BY 4.0 |
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