txt39 B

OCR Hard GT: Low Receipt

Exact ground-truth string for the low-receipt OCR hard case.

Preview, first 2 linestxt

RECEIPT #RX-SAMPLE-009 Subtotal 48.20

Specifications

Kind: low-contrast
Text: RECEIPT #RX-SAMPLE-009 Subtotal 48.20
Role: ground truth
Alt Text: Exact ground-truth string for the low-receipt OCR hard case
Alt Text Source: description

Testing contract

Reference control

Scenario: Exercise OCR Hard GT: Low Receipt in its ocr hard workflow. Exact ground-truth string for the low-receipt OCR hard case.
Expected result: 1 text lines, decoded as UTF-8; first nonempty line is 'RECEIPT #RX-SAMPLE-009 Subtotal 48.20'. Declared feature checks: kind=low-contrast; text=RECEIPT #RX-SAMPLE-009 Subtotal 48.20; role=ground truth. The declared comparison counterpart is img-we-ocr-low-receipt-img; preserve the stated difference instead of expecting the container bytes to match.

What is a .txt file?

TXT is a plain-text file containing unformatted character data with no styling or structure beyond line breaks. Its interpretation depends on character encoding, most commonly UTF-8, and on line-ending convention. It is the most universal and portable text container.

How to use this file

Use an example TXT to test encoding detection, line-ending (LF versus CRLF) handling, and any tool that reads or streams raw text input.

How to use this file for testing

“OCR Hard GT: Low Receipt” is a deterministic Testaroo fixture for OCR testing. Image-only 'scanned' documents paired with their text source, for measuring OCR accuracy against a known ground truth.

Documented properties for this file: ground truth. Compare results against paired or grouped companions on this page when present (clean↔damaged, searchable↔scanned, or format twins) so scores stay reproducible across runs.

Download the file once, keep the path stable in CI or local scripts, and treat the spec table as the contract: dimensions, seeds, field lists, and roles are intentional. Corrupt or invalid samples are labelled as such, expect parsers to fail loudly rather than silently accept them.

For image AI or enhancement tools, run the model or filter on this file and diff against the clean or ground-truth companion in the same group when available. Keep seeds and documented damage parameters in your evaluation notes so regressions are attributable.

Run OCR on the scanned or image twin and score the output against the searchable or text ground truth on this page; the documented rotation, grain, and text content are the reference.

Generated by generation/images_wave_e.py. Free for any use, no attribution required, license.