Guides & articles

We gave space-ocr and Mistral Document AI the same 8 hard documents, 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.

Most image-to-Markdown tools hand you a string and no way to check it. Here's what layout-preserving Markdown OCR looks like when every element keeps its coordinates.

Raw OCR returns paragraphs in detection order, so two-column pages come back interleaved. What it takes to get plain text you can actually index.

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 a verified box, a match_ratio, and a row you can query.

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, match_ratio verification, and audit-ready data pipelines you can actually trust.

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.

Use per-field bounding boxes and match ratios to spot-check extracted data fast — without re-running OCR.

Declare the line-item table as an array field and get one structured, verifiable row per item — then expand it straight into 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 — verifiable per-value coordinates, 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 only says where, not how sure. How to get source coordinates plus a per-value match ratio you can verify.

Google Cloud Vision is excellent raw OCR — text plus pixel boundingPoly and a recognition confidence — but structured key-value fields are a separate product (Document AI). Here's the honest dividing line.

Receipt OCR three ways: self-hosted Tesseract, cloud Google Vision, and verification-first space-ocr. Where each wins — pixel boxes vs. structured fields, recognition confidence vs. character-coverage match ratio.

An OCR API where every value carries source coordinates and a match_ratio you can gate on — flag low-confidence values for review, keep an audit trail, and query stored results server-side without re-running OCR.

How to ask the OCR API for invoice line items as a structured array, the exact JSON you get back (each cell with its own bbox, vertices and match_ratio), and how to parse it in code and unfold it 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.