Text recognition pipeline in Computer Vision

Introduction

Text recognition pipeline helps computers read and understand text from images or photos. It turns pictures of words into editable and searchable text.

Reading text from scanned documents to digitize paper files.

Extracting text from photos of street signs or menus for navigation apps.

Converting handwritten notes into digital text for easier editing.

Helping visually impaired people by reading text aloud from images.

Automatically processing invoices or receipts in businesses.

Syntax

Computer Vision

1. Input image
2. Preprocessing (resize, grayscale, noise removal)
3. Text detection (find text areas)
4. Text segmentation (split text into characters or words)
5. Text recognition (convert images of text to characters)
6. Postprocessing (correct errors, format text)
7. Output recognized text

Each step can use different methods or models depending on the task.

Preprocessing improves image quality for better recognition.

Examples

A simple pipeline for recognizing printed text in photos.

Computer Vision

Input image -> Grayscale -> Detect text boxes -> Recognize text in boxes -> Output text

Pipeline including noise removal and spelling correction for handwritten notes.

Computer Vision

Input image -> Resize -> Remove noise -> Segment characters -> Use OCR model -> Correct spelling -> Final text

Using a recurrent neural network (RNN) to read lines of text in order.

Computer Vision

Input image -> Detect lines of text -> Recognize each line with RNN -> Combine lines -> Output full text

Sample Model

This code reads an image, converts it to grayscale, applies thresholding to highlight text, and uses pytesseract OCR to recognize text. It then prints the recognized text.

Computer Vision

import cv2
import pytesseract

# Load image
image = cv2.imread('sample_text.jpg')

# Convert to grayscale
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# Apply thresholding to get binary image
_, thresh = cv2.threshold(gray, 150, 255, cv2.THRESH_BINARY)

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

print('Recognized Text:')
print(text.strip())

Important Notes

Good image quality improves recognition accuracy.

Text detection helps focus recognition only on text areas.

Postprocessing like spell check can fix recognition mistakes.

Summary

Text recognition pipeline converts images of text into editable text.

It includes steps like preprocessing, detection, recognition, and postprocessing.

Simple tools like pytesseract can perform OCR on images easily.