Amazon Textract AnalyzeExpense vs Tesseract OCR

Amazon Textract AnalyzeExpense extracts financial text and data from invoices and receipts through an API or web service. Results can include grouped line items and summary fields such as purchased items, prices, receipt headers, and vendor names. The API can normalize field names and column headers to help downstream applications compare documents. It can also identify vendors from logos and extract items, quantities, and prices when those details have no labels or column headers. Supply documents as image bytes or an Amazon S3 object; AWS advises PDF, JPG, or PNG files, and invoice and receipt processing is English only. AnalyzeExpense processes synchronously. Asynchronous invoice and receipt analysis uses StartExpenseAnalysis and GetExpenseAnalysis. The listed rate is $0.01 per page for the first 1 million pages per month in US West (Oregon), then $0.008 per page; custom high-volume pricing is available. New AWS customers can use the Free Tier for three months, up to 100 pages per month. AWS says inputs may be used to improve Textract and related AI technologies, with an Organizations opt-out policy.

Tesseract OCR is a free, open-source engine that extracts printed text from images. It can be run from the command line or used through an API; the project does not include a graphical application. It recognizes more than 100 languages out of the box and supports Unicode UTF-8. Version 4 introduced an LSTM neural-network engine for line recognition while retaining the legacy character-pattern engine. Input formats include PNG, JPEG, TIFF, JPEG 2000, GIF, WebP, BMP, and PNM. Output formats include plain text, hOCR, PDF, TSV, ALTO, and PAGE. Tesseract cannot read PDF files directly, although PDF is available as an output format; the documentation suggests converting files or using OCRmyPDF. The project documents wrappers for several languages, including Python, Java, Swift, Flutter, Ruby, Rust, and Go, as well as C and C++ APIs. The repository code uses Apache License 2.0, though dependencies may have different licenses. Image quality may need improvement for better recognition results.