receipt-scanner

To extract data from receipt images into JSON or CSV, you process JPG, PNG, or PDF files using OCR and pattern matching to identify the vendor, date, line items, and total amounts. This automation outputs structured digital formats for expense reporting.

Automating invoice processing for expense tracking involves using OCR to extract text from physical receipts and pattern matching to capture vendor, date, and total amounts. The extracted data is structured into JSON or CSV files for easy integration.

Yes, this OCR receipt scanner works with PDF formats in addition to JPG and PNG images. It uses pytesseract and opencv-python to recognize text and extract structured data like vendor, date, and total amounts from these supported file types.

Yes, you need pytesseract and pandas along with pillow, opencv-python, and numpy to extract structured receipt data. These dependencies are required for text recognition, image processing, and manipulating the extracted data into JSON or CSV outputs.

OCR can extract specific data fields from a receipt including the vendor name, transaction date, individual line items, tax, and total amounts. It uses pattern matching to identify this structured data from the recognized text.

Yes, you can use opencv-python combined with pytesseract to process physical receipts for expense reporting. This workflow captures text from images or PDFs and converts the extracted vendor and total amount data into structured JSON or CSV files.