Guides and pricing comparisons on OCR, AI document data extraction, and intelligent document processing for teams evaluating tools.
Extract text from a PDF in PHP: read the text layer with smalot/pdfparser, or OCR a scanned PDF with an API. Code for both, plus how to handle them in one script.
Read articleExtract text from an image in JavaScript: run tesseract.js for free client-side OCR, or call an OCR API for structured fields. Code, tradeoffs and when to use each.
Read articleExtract text from a PDF in Python: use pdfplumber or PyMuPDF for digital PDFs, and OCR (pytesseract or an OCR API) for scanned PDFs. Code, tradeoffs and when to use each.
Read articleExtract tables from a PDF in Python: Camelot and tabula-py for digital PDFs, an OCR API for scanned PDFs. Keep rows and columns aligned and export to CSV, Excel or JSON.
Read articleOCR accuracy is 98 to 99.5 percent on clean printed pages and lower on scans, handwriting and tables. There is no single number: it depends on the document, engine and metric.
Read articleThe Google Document AI rate limit is set by adjustable quotas, including 5 concurrent batch requests per project and provisioned throughput of about 120 pages per minute. Here is how to read the quotas, fix RESOURCE_EXHAUSTED errors, and scale.
Read articleThe Azure Document Intelligence file size limit is 500 MB on the paid S0 tier and 4 MB on the free F0 tier. Here is what counts against it, why F0 catches teams, and how to handle files over the cap.
Read articleThe Document AI page limit exceeded error means your document is over the 15-page online cap. Here is exactly why it fires, the 40 MB file limit behind it, and how to move to batch processing for up to 1,000 pages.
Read articleAWS Textract reads one page synchronously and up to 3,000 pages asynchronously. Here is the exact page and file-size limit for each call type, why a multi-page PDF fails on the sync call, and how to read a long document.
Read articleExtracting data from a check means reading the MICR line for the routing and account numbers, the courtesy and legal amounts, the payee and the date, then validating them. Here is how check OCR works, what breaks, and how to do it reliably.
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