| --- |
| license: apache-2.0 |
| language: |
| - en |
| pipeline_tag: image-to-text |
| library_name: onnx |
| tags: |
| - ocr |
| - document-understanding |
| - document-parsing |
| - key-information-extraction |
| - receipts |
| - invoices |
| - pdf |
| - onnx |
| - cpu |
| datasets: |
| - naver-clova-ix/cord-v2 |
| - docling-project/DocLayNet-v1.2 |
| metrics: |
| - cer |
| model-index: |
| - name: tinydoc |
| results: |
| - task: |
| type: image-to-text |
| name: OCR |
| dataset: |
| name: OmniDocBench v1.5, English pages |
| type: opendatalab/OmniDocBench |
| metrics: |
| - type: edit_distance |
| value: 0.117 |
| name: Text edit distance, page average (lower is better) |
| - task: |
| type: image-to-text |
| name: Text-line recognition |
| dataset: |
| name: CORD-v2 receipt lines |
| type: naver-clova-ix/cord-v2 |
| split: test |
| metrics: |
| - type: cer |
| value: 1.23 |
| name: Character error rate, % (lower is better) |
| - task: |
| type: image-to-text |
| name: Text-line recognition |
| dataset: |
| name: DocLayNet v1.2 lines |
| type: docling-project/DocLayNet-v1.2 |
| split: test |
| metrics: |
| - type: cer |
| value: 8.94 |
| name: Character error rate, % (lower is better) |
| --- |
| |
|  |
|
|
| A small OCR model (5.1M parameters, 20 MB) that reads printed receipts, invoices, PDFs and ordinary pages |
| on a CPU. It returns the text and the key fields (merchant, date, totals, line items) as JSON, with a |
| confidence for every value and a pointer to where on the page it was read. |
|
|
| ## Benchmarks |
|
|
|  |
|
|
| Measured on an Apple M5 Pro, CPU only, with the same pages and crops for every engine. Indra OCR's |
| figures are the ones Datalab published; Indra was not run here. |
|
|
| ## Quick start |
|
|
| ```bash |
| pip install -r requirements.txt |
| python -m tinydoc extract receipt.jpg invoice.pdf --backend onnx --weights onnx --out result.json |
| ``` |
|
|
| Every field has a `confidence` and a `needs_review` flag. Missing values are `null`, never guessed. |
| Check `validation.needs_review` before trusting totals. |
|
|
| ## Training data |
|
|
| | Source | Licence | |
| |---|---| |
| | [CORD-v2](https://huggingface.co/datasets/naver-clova-ix/cord-v2) (Park et al., 2019) | CC BY 4.0 | |
| | [WildReceipt](https://download.openmmlab.com/mmocr/data/wildreceipt.tar) (Sun et al., 2021) | Apache 2.0 | |
| | [DocLayNet v1.2](https://huggingface.co/datasets/docling-project/DocLayNet-v1.2) subset (Pfitzmann et al., 2022) | CDLA-Permissive-1.0 | |
| | Synthetic receipts, invoices and pages | rendered with OFL, Apache and UFL fonts | |
|
|
| Trained from scratch: no pretrained weights, and nothing distilled from another model. |
|
|
| ## Limitations |
|
|
| - Printed Latin-script text only; no handwriting. |
| - Table structure is weak (OmniDocBench TEDS 0.29). |
| - With several workers in parallel, Tesseract is slightly faster (3.26 against 3.17 pages per second |
| with 4 workers) and uses far less memory (120 MB against 1.3 GB per worker). |
| - Line items and unfamiliar invoice layouts are the least reliable fields. |
| - Dense multi-column pages (newspapers, magazines) are the hardest for reading order. |
|
|
| ## Licence |
|
|
| Apache 2.0 (see `LICENSE`). The training data keeps its own licences, listed above. |
|
|
