Automatically extract table data from PDFs, receipts, and document images. Recognize text with OCR and download structured data as CSV or JSON. Perfect for expense reports, data entry, and document digitization.
Drag or click to upload PDF or image
PDF, JPG, PNG supported (Max 20MB)Upload a PDF or receipt/document image (JPG, PNG). Any format containing a table can be processed. Clearer images produce more accurate results.
Choose the language used in the document. Supports Korean, English, and Japanese. Use auto-detect for mixed-language documents. Accurate language selection improves recognition accuracy.
Click "Extract Table" to start OCR. PDFs are first converted to images before text recognition. Real-time progress is displayed.
Edit the extracted table directly in the preview. Click any cell to modify it, add or delete rows/columns. Download the final data as CSV or JSON, or copy it to the clipboard.
Photograph restaurant, grocery, or online shopping receipts to automatically extract items, quantities, and amounts into a table. Dramatically reduces time spent on expense reports.
Upload PDF bank statements or card bills to extract transaction data as CSV. Paste directly into Excel or accounting software for instant analysis.
Digitize tabular data from paper documents — price lists, schedules, grade sheets, and more. Get automatically structured data without manual entry, greatly improving work efficiency.
Extract research data tables from academic papers or report PDFs for further analysis. Even tables embedded as images can be converted to text data via OCR.
PDF, JPG, and PNG formats are supported. PDFs are converted page-by-page to images before OCR processing. Both scanned and digitally created PDFs are supported, though digital PDFs yield higher accuracy. Maximum file size is 20MB.
Over 95% accuracy is achieved on clear printed documents. Handwriting or low-quality images may have lower recognition rates. Table structure detection works best when borders are clear and cells are well-defined. Errors can be corrected using the built-in edit feature.
All processing happens in your browser and no files are sent to any server. Receipts, statements, and other documents containing personal information are processed securely. Uploaded files and extracted data are deleted when you refresh the page.
CSV can be opened directly in Excel, Google Sheets, and other spreadsheet programs — ideal for data analysis and editing. JSON is suited for web developers or programmatic data processing. For general users, CSV is recommended.
Try improving image quality or changing the OCR language and run again. The extracted table supports direct editing — you can modify cell contents and add or delete rows and columns. Manual editing may be needed for complex or irregular table structures.
The current version processes the first page of a PDF. For multi-page documents, split the PDF using a PDF splitter tool and process each page individually, or convert the PDF to images and upload each page in sequence.
Combines OCR (Optical Character Recognition) and PDF rendering to automatically extract table data from document images. Fully browser-based for complete privacy, with easy export to CSV and JSON formats.
OCR-based table extraction recognizes characters in images and analyzes position data to reconstruct row-and-column structures. The Tesseract OCR engine, developed by Google, supports over 100 languages and uses a deep learning-based LSTM neural network for high accuracy. PDF.js, developed by Mozilla, is a PDF rendering library that converts PDFs to images directly in the browser. Combining these technologies enables efficient extraction of tables from a wide variety of document formats.
Table extraction technology plays a critical role in financial data automation, document digitization, and data migration. Expense management departments can process hundreds of receipts automatically, reducing data entry time by over 90%. Accounting teams can automatically structure bank statements and card records for direct import into accounting software. Researchers can easily extract data tables from papers for meta-analysis and secondary research.
Image quality is the most important factor for the best extraction results. Recommended conditions include: scanned or photographed images at 300 DPI or higher, documents with clearly visible table borders, and strong contrast between background and text. Horizontally aligned images without skew and clean prints without smudging produce the best results. After extraction, use the edit feature to fix recognition errors and add rows or columns as needed to improve completeness.