AI Agents · Lesson

OCR for Scanned Documents

Tesseract via pytesseract, image preprocessing, and accuracy improvement.

When OCR Is Necessary

Not all documents are digital-native PDFs. Scanned documents, photographs of text, handwritten notes, and image-based PDFs require Optical Character Recognition (OCR) to extract text.

OCR converts pixel images of text into machine-readable characters. Two leading Python libraries: pytesseract (Google Tesseract) and EasyOCR (deep learning, multilingual).

pytesseract Basics

pytesseract is a Python wrapper around Google's Tesseract OCR engine. Install Tesseract first (OS-level), then pip install pytesseract Pillow.

import pytesseract
from PIL import Image

# Simple text extraction
image = Image.open('scanned_document.png')
text = pytesseract.image_to_string(image)
print(text)

# Specify language (default: English)
text_fr = pytesseract.image_to_string(
    Image.open('french_doc.png'),
    lang='fra'
)

# Get detailed output with bounding boxes
data = pytesseract.image_to_data(
    image,
    output_type=pytesseract.Output.DICT
)
for i, word in enumerate(data['text']):
    if word.strip():
        conf = data['conf'][i]
        print(f'Word: {word!r:20} Confidence: {conf}')

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