Ocr And Documents

OCR and Document Text Extraction

Extract readable text from PDFs, scanned images, and Word documents using Python libraries available in most environments. No cloud API required.

When to Use

• User wants to read text from a PDF file
• User wants to extract text from a scanned image or photo of a document
• User wants to read a .docx Word document programmatically
• User wants to convert a multi-page document to plain text for analysis
• User wants to extract specific pages or sections from a PDF

How to Use

1. Extract text from a PDF (pymupdf — fastest)

pythonCopy code
import fitz  # pip install pymupdf

doc  = fitz.open("document.pdf")
text = "\n\n".join(page.get_text() for page in doc)
print(text[:2000])  # preview first 2000 chars
doc.close()

2. Extract text from a PDF (pdf-parse via Node.js)

javascriptCopy code
// requires: npm install pdf-parse (already in DevOS dependencies)
const pdfParse = require('pdf-parse')
const fs       = require('fs')
const data     = await pdfParse(fs.readFileSync('document.pdf'))
console.log(data.text.slice(0, 2000))
console.log(`Pages: ${data.numpages}`)

3. OCR a scanned image (Tesseract)

Requires Tesseract installed: winget install UB-Mannheim.TesseractOCR

pythonCopy code
import pytesseract          # pip install pytesseract
from PIL import Image       # pip install Pillow

img  = Image.open("scan.png")
text = pytesseract.image_to_string(img, lang="eng")
print(text)

4. OCR with preprocessing for better accuracy

pythonCopy code
import pytesseract
from PIL import Image, ImageFilter, ImageOps

img = Image.open("scan.jpg")
img = ImageOps.grayscale(img)
img = img.filter(ImageFilter.SHARPEN)
img = img.point(lambda p: 255 if p > 128 else 0)  # binarize
text = pytesseract.image_to_string(img, config="--psm 6")
print(text)

5. Extract text from a Word .docx file

pythonCopy code
from docx import Document   # pip install python-docx

doc   = Document("report.docx")
paras = [p.text for p in doc.paragraphs if p.text.strip()]
text  = "\n".join(paras)
print(text)

6. Extract a specific page range from a PDF

pythonCopy code
import fitz

doc    = fitz.open("big_report.pdf")
pages  = range(4, 9)   # pages 5-9 (0-indexed)
text   = "\n\n".join(doc[i].get_text() for i in pages)
print(text)

7. Extract tables from a PDF

pythonCopy code
import pdfplumber   # pip install pdfplumber

with pdfplumber.open("financial_report.pdf") as pdf:
  for page in pdf.pages:
    for table in page.extract_tables():
      for row in table:
        print("\t".join(str(cell or "") for cell in row))

Examples

"Read the text from this PDF contract" → Use step 1 (pymupdf) or step 2 (pdf-parse) depending on whether Python or Node is preferred.

"Extract the table from page 3 of this quarterly report PDF" → Use step 7 (pdfplumber) targeting pdf.pages[2] for page 3.

"Read the text from this scanned invoice image" → Use step 3 or 4 (Tesseract). For low-quality scans, use step 4 with preprocessing.

Cautions

• Scanned PDFs (image-only) have no embedded text — Tesseract OCR is required
• Tesseract accuracy drops on handwriting, decorative fonts, or low-resolution images (< 150 DPI)
• pymupdf (fitz) extracts only programmatically embedded text — it won't OCR scanned pages
• Large PDFs can use significant memory — process page by page for files > 100 MB
• For non-English text, specify the language code in Tesseract: lang="hin" for Hindi, "deu" for German