pagelens-ocr

OCR for scanned documents that runs on a normal CPU - no GPU, no cloud. Give it a PDF, JPG, JPEG or PNG (the type is detected automatically) and it prints the text of every page, keeping headings, side-by-side fields and tables in place.

Install

Python 3.10 or newer, then:

pip install pagelens-ocr

The first run downloads the OCR models once (~250 MB, needs internet); after that it works offline.

Use from the command line

pagelens-ocr scan.pdf
pagelens-ocr photo.jpg
pagelens-ocr scan.pdf > scan.txt        # save the text to a file

Output - the file name and page number, then that page's text:

scan.pdf - Page 1
# INVOICE
Invoice No   : 1234                    Date : 01/01/2025
| Item     | Qty | Amount |
|----------|-----|--------|
| Paper A4 | 2   | 500.00 |

scan.pdf - Page 2
...

Use from Python

from pagelens_ocr import extract, extract_text

for page in extract("scan.pdf"):          # one result per page
    print(page["file"], page["page"], page["seconds"])
    print(page["text"])

text = extract_text("photo.jpg")          # all pages as one string

What it does

Per page: crop scanner borders, straighten tilted scans, remove punch holes -> detect and read every text line (PP-OCRv6; a fast model reads every line, a stronger one re-reads uncertain lines) -> find headings, text blocks, tables and logos (PP-DocLayout v2) -> rebuild table rows and columns (SLANet+) -> print in reading order. Typically 4-9 seconds per page on a laptop CPU.

Handwriting is not supported. Always check important values (numbers, dates, IDs) against the original.

License

Apache-2.0