Text recognition pipeline in Computer Vision - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to load an image for text recognition.

Computer Vision

from PIL import Image
image = Image.open([1])

Drag options to blanks, or click blank then click option'

Aopen('text_image.jpg')

Btext_image.jpg

CImage('text_image.jpg')

D'text_image.jpg'

Attempts:

3 left

💡 Hint

Common Mistakes

Forgetting quotes around the filename.

Passing an Image object instead of a filename string.

The Image.open() function requires a string path to the image file, so the filename must be in quotes.

2fill in blank

medium

Complete the code to convert the image to grayscale before text recognition.

Computer Vision

gray_image = image.convert([1])

Drag options to blanks, or click blank then click option'

A'CMYK'

B'RGBA'

C'L'

D'RGB'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'RGB' instead of 'L' for grayscale conversion.

Using modes that keep color channels.

The mode 'L' converts the image to grayscale, which is common preprocessing for text recognition.

3fill in blank

hard

Fix the error in the code to extract text from the image using pytesseract.

Computer Vision

import pytesseract
text = pytesseract.image_to_string([1])

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Agray_image

Bimage_path

Cimage

Dopen('text_image.jpg')

Attempts:

3 left

💡 Hint

Common Mistakes

Passing a file path string instead of an image object.

Passing a file object instead of an image.

pytesseract.image_to_string expects an image object, here the grayscale image is best for accurate text extraction.

4fill in blank

hard

Fill both blanks to create a dictionary of word lengths for words longer than 3 characters.

Computer Vision

words = text.split()
lengths = {word: [1] for word in words if len(word) [2] 3}

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Alen(word)

B>

C<

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using word instead of len(word) for length.

Using '<' instead of '>' for filtering.

We use len(word) to get the length, and filter words with length greater than 3 using '>'.

5fill in blank

hard

Fill all three blanks to create a dictionary of uppercase words and their lengths for words longer than 4 characters.

Computer Vision

words = text.split()
result = { [1]: [2] for w in words if len(w) [3] 4}

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Aw.upper()

Blen(w)

C>

Dw

Attempts:

3 left

💡 Hint

Common Mistakes

Using w instead of w.upper() for keys.

Using '<' instead of '>' for filtering.

We convert words to uppercase for keys, use their length as values, and filter words longer than 4 characters.