Guides & articles

Round one used hard documents. Production sends worse photos. So we added them: a sideways dot-matrix slip, overlapping carbon copies, a crumpled note in bad light, a night shot on a clipboard, and a receipt that prints the same ¥711 four times.

We gave space-ocr and Mistral Document AI the same 8 test cases, the same field schemas, and the same scorer. The score gap was large. The structural gap was larger.

Saying a pipeline verifies itself is a claim until you turn it off. We did: 22 cells fixed, 0 broken, and a flag that is right 75% of the time — with a measurement method that transfers to any extraction pipeline.

A current image-to-Markdown OCR workflow: keep the assembled text, map elements and table cells back to the page, and route only flagged paths to review.

Raw OCR can interleave multi-column pages. Learn the current values, cells, review, image, and source contract for text you can index and verify.

Stop manually typing data from photos of documents. space-ocr automatically handles tilted images and mixed languages to give you clean spreadsheet rows from any JPEG or PNG.

Prompting GPT-4o or Gemini to read a document gives you text you cannot check or query. space-ocr returns each value with its box and quad, a verified verdict, and a review.flagged list of what to look at.

How to turn unstructured PDFs into machine-readable CSVs — render each page to an image, extract named fields with verified on-page bounding boxes, and export clean UTF-8 CSV that drops straight into a database.

How to implement a structured-field OCR API with bounding boxes in 2026 — normalized 0–1000 coordinates on every value, a verified verdict beside each one, and a review list your pipeline can act on.

Find low cost OCR for startups. This guide shows how to build a scalable document pipeline without enterprise bloat or per-seat fees — pay only for what you use.

Stop copying and pasting from images of tables. Define your columns once, and space-ocr will turn each line item into a structured CSV row, ready for import.

Stop polling and build a modern AP workflow. Upload invoices to our OCR API asynchronously and get structured data pushed directly to your endpoint via signed webhooks.

Go from a chaotic folder of JPEGs to a structured workspace. Organize your scanned documents into sheets, where every scan is a row, every value is verifiable, and everything is searchable.

Stop manually typing receipts into spreadsheets. Scan your documents, extract structured data, and import them into Google Sheets as clean, auditable rows.

Stop wrestling with complex OCR libraries. This guide shows you how to parse any invoice image into structured JSON using a simple Python script and a single REST API call.

Turn that stack of paper receipts into a clean, verifiable CSV for your expense reports. Here’s a practical guide to digitizing receipts, complete with on-page verification.

Start with review.flagged, open cells[path], and confirm each extracted value against its box or quad on the source image.

Declare the line-item table as an array field: every row gets a path in data.cells with its coordinates and verdict, and data.review.flagged lists what to check before it becomes CSV.

A scanned page is an image, not a spreadsheet — here's how to read it into structured rows and export a CSV that opens directly in Excel.

When a Textract alternative makes sense — path-keyed source coordinates, a review list that says what to check, Japanese/Korean/Chinese support, a queryable sheet, and flat per-image pricing with no AWS account — and where Textract still shines.

Most OCR APIs return bounding boxes, but coordinate systems differ and a box alone says where, not whether the value holds up. How to get a 0–1000 box and quad for every field you declare, plus data.review.flagged as the review queue.

Cloud Vision returns OCR text and geometry. Document AI is Google's processor-based route to forms and invoices. space-ocr adds a per-request field schema and an explicit review contract.

Receipt OCR three ways: self-hosted Tesseract, cloud Google Vision, and verification-first space-ocr. Where each wins — pixel boxes vs. per-value coordinates in data.cells, recognition confidence vs. a review queue backed by character-coverage evidence.

Use path-keyed cells for source coordinates and data.review.flagged as the human-review queue, with match ratio retained only as supporting evidence.

Define invoice rows as an array field, read business data from values, inspect source coordinates through path-keyed cells, and route review.flagged items before exporting to CSV.

A scanned PDF is a picture of a table. Skip the retyping and the copy-paste: read each page into structured fields and write it out to a CSV (UTF-8 BOM) that opens straight in Excel. Here's the real-world workflow.