๐๐ผ๐ ๐ ๐๐๐๐ผ๐บ๐ฎ๐๐ฒ๐ฑ ๐๐ป๐๐ผ๐ถ๐ฐ๐ฒ ๐ฃ๐ฟ๐ผ๐ฐ๐ฒ๐๐๐ถ๐ป๐ด
A few months ago, I faced a massive task. I had to process dozens of invoices from different vendors.
The volume was not the problem. The different formats caused the trouble.
Vendors sent files in various ways:
- PDF files
- Excel spreadsheets
- HTML tables
I needed all this data in one CSV file. Copying and pasting every line felt slow. It led to many mistakes.
I tried manual entry first. I opened each file and typed the data. This method worked for one or two files. It failed when the workload grew.
I decided to build an automation script. My workflow followed these steps:
- Scan the invoice folder.
- Read the data from each file.
- Merge all information into one list.
- Export the final result to a single file.
The logic is simple: from pathlib import Path import pandas as pd
invoice_dir = Path("invoices") all_data = []
for file in invoice_dir.glob("*"): # Logic to read file goes here all_data.append(new_data)
combined = pd.concat(all_data, ignore_index=True) print("Done!")
This small change changed my routine. I no longer waste time on repetitive data entry. I save hours every month.
Stop manual entry when you find a pattern. Use scripts to handle the boring work.
How do you handle document extraction in your projects? Do you use OCR, custom scripts, or third-party tools?
Source: https://dev.to/dylan_parker123/how-i-automated-invoice-processing-instead-of-copy-pasting-data-2002
Optional learning community: https://t.me/GyaanSetuAi