Extract Accurate Text from Documents Faster with OCR API

Our layout-aware page reconstruction preserves reading order across columns, headers/footers, and mixed content blocks. You get coherent text and structure without writing brittle post-processing rules. That means faster integration and fewer edge-case failures in production.

Yes—tables are detected and reconstructed as true table structures rather than flattened into a paragraph of text. We support complex layouts like merged headers and nested cells, and can return tables in machine-ready formats such as Markdown or structured data. This makes downstream analytics, RPA, and data pipelines far more reliable.

It’s more than text: we interpret common visual elements like charts and formulas and convert them into usable representations such as Markdown tables, Mermaid, or LaTeX. This helps you capture the evidence embedded in visuals instead of losing it during extraction. It’s ideal for reports, financial statements, and technical documents.

You can request structured JSON that includes element types, page numbers, and spatial coordinates for each extracted block. This makes it easy to build UI overlays, citations, selective redaction, and “jump to source” experiences. You avoid building a separate document mapping layer from scratch.

Basic OCR often returns raw text that requires extensive cleanup for layouts, tables, and reading order. By returning structured, layout-aware output upfront, we minimize custom heuristics and reprocessing loops. Teams typically ship document workflows faster and spend less time debugging edge cases.

Send a document and choose the output you need—clean text, reconstructed tables, or structured JSON with metadata. The response is designed to be immediately usable in search, extraction pipelines, and review tools without heavy transformation. If you’re evaluating, you can start small and expand to richer outputs as your use case grows.