Why OCR digitizes text from images in Computer Vision

Introduction

OCR turns pictures of words into real text you can edit and search. It helps computers understand written information from images.

You want to copy text from a photo of a book page.

You need to search words inside scanned documents.

You want to convert handwritten notes into editable text.

You need to extract text from signs or labels in pictures.

You want to automate data entry from printed forms.

Syntax

Computer Vision

No specific code syntax; OCR is a process that takes an image as input and outputs text.

OCR systems usually take images like JPG or PNG files.

The output is editable text like plain text or searchable PDFs.

Examples

OCR reads the receipt image and extracts the total amount as text.

Computer Vision

Input: photo_of_receipt.jpg
Output: "Total: $23.45"

OCR converts the scanned page into editable text for reading or searching.

Computer Vision

Input: scanned_book_page.png
Output: "Chapter 1: Introduction to AI"

Sample Model

This program loads an image file containing text and uses the pytesseract library to extract the text. It then prints the text so you can see what was read from the image.

Computer Vision

from PIL import Image
import pytesseract

# Load image with text
image = Image.open('sample_text_image.png')

# Use pytesseract to do OCR
text = pytesseract.image_to_string(image)

print('Extracted text:')
print(text.strip())

Important Notes

OCR accuracy depends on image quality and text clarity.

Good lighting and clear fonts improve OCR results.

OCR can struggle with handwriting or unusual fonts.

Summary

OCR changes images of text into editable, searchable text.

This helps computers read and use written information from pictures.

It is useful for digitizing books, receipts, signs, and more.