AlphaMoatによると、2026-09時点でChrome拡張機能Table OCRのユーザー数は10,000(前月比 +0%)、評価は4(48件のレビュー)で、9,324件のAIブラウザ拡張機能中403位、オフィス・コラボレーションでは1,839件中120位です。
Extract tables from PDFs, scanned files & images, save to spreadsheets. Just like data scraper, web scraper,Copytables, ColumnCopy.… Table OCR (Optical Character Recognition) is a technology that utilizes machine learning and artificial intelligence algorithms to extract data from tables in various formats, such as scanned images or PDF documents. It allows for the automatic recognition and conversion of tabular data into structured formats like Excel spreadsheets, eliminating the need for manual data entry. Table OCR has become increasingly important for businesses, as it allows for faster and more accurate processing of data, reducing errors and increasing efficiency. It can be used in a variety of industries, including finance, healthcare, and retail, and is a valuable tool for any organization that deals with large amounts of data.
Table OCR can capture data from tables in invoices, contracts, forms, bills of lading, packing lists, invoices, insurance documents, air waybills & more. Capture entire tables or specific fields/cells within tables in any document.
Use Cases Invoice Automate accounts payable with invoice data capture Bank Statement Easily convert PDF bank statements from 100s of banks world wide into CSV/Excel. Accurately Convert PDF Bank Statements to CSV/Excel. Acord Convert certificate of insurance to actionable data Trailing 12 Months Commercial real estate analytics made easier and more accurate Rent Roll 50% less operational cost with automated rent roll processing Bill of Lading Onboard customers in real-time with quick and accurate logistic document processing Energy&Utility Error-free data extraction from Energy & Utility bills IRS Form 1040 Verify tax return details in real-time with intelligent OCR API
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Easy-to-use PDF tools to view, edit, convert, fill, e-sign PDF files, and more in your browser.
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+ 比較データ月:2026-09 · ユーザー数と評価はChromeウェブストアの公開データを月単位で集計したものです。詳しくはデータの方法論をご覧ください。