| --- |
| title: Document Understanding OCR |
| emoji: 📄 |
| colorFrom: yellow |
| colorTo: blue |
| sdk: docker |
| pinned: false |
| license: mit |
| --- |
| |
| # Document Understanding OCR |
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| ## Question |
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| After OCR has produced text, how do we recover a structured document schema? |
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| ## System Boundary |
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| This Streamlit Space demonstrates the post-OCR layer for invoices: field extraction, confidence scoring, line-item parsing, validation, and JSON export. |
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| ## Method |
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| The app applies transparent extraction patterns to OCR text, computes field-level confidence, parses line items, and compares extracted fields against a review threshold. |
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| ## Technique |
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| This is schema extraction after OCR. Raw text is mapped into named fields, and each field gets a confidence signal. |
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| The method is intentionally transparent: field patterns are visible and the review threshold controls which fields require human attention. |
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| ## Output |
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| The app returns a field table, line-item table, confidence chart, review queue, and JSON payload. |
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| ## Why It Matters |
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| Document AI becomes useful when extraction is inspectable. A human reviewer should know which fields were found, which were uncertain, and what JSON would be sent downstream. |
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| ## What To Notice |
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| Field-level confidence is more actionable than a single document score. A document can be mostly correct while one critical field, such as total or due date, is wrong. |
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| ## Effect In Practice |
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| This pattern supports invoice processing, procurement workflows, form extraction, and human review queues. |
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| ## Hugging Face Extension |
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| The Space can add document-image OCR with TrOCR, Donut, LayoutLM, or a vision-language model and evaluate field-level extraction accuracy. |
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| ## Limitations |
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| This version starts from OCR text. A full system should add image-to-text OCR or document VLM inference, table recognition, multilingual support, and labeled evaluation. |
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| ## Run Locally |
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| ```bash |
| pip install -r requirements.txt |
| streamlit run app.py |
| ``` |
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|
