Text detection in images in Computer Vision - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Understanding Text Detection Models

Which of the following best describes the main goal of a text detection model in images?

A Classify the type of font used in the text regions.

B Identify and locate regions in the image that contain text.

C Translate the detected text into another language.

D Enhance the image quality to make text clearer.

💡 Hint

Think about what 'detection' means in the context of images.

Text detection models focus on finding where text appears in an image, not on reading or translating it.

❓ Predict Output

intermediate

Output of Text Region Coordinates Extraction

What is the output of the following Python code snippet using OpenCV's EAST text detector after processing an image?

Computer Vision

import cv2
import numpy as np

# Assume 'image' is a loaded image
net = cv2.dnn.readNet('frozen_east_text_detection.pb')
blob = cv2.dnn.blobFromImage(image, 1.0, (320, 320), (123.68, 116.78, 103.94), True, False)
net.setInput(blob)
scores, geometry = net.forward(['feature_fusion/Conv_7/Sigmoid', 'feature_fusion/concat_3'])

# Process scores and geometry to get boxes
conf_threshold = 0.5
boxes = []
for y in range(scores.shape[2]):
    for x in range(scores.shape[3]):
        score = scores[0, 0, y, x]
        if score < conf_threshold:
            continue
        offsetX, offsetY = x * 4.0, y * 4.0
        angle = geometry[0, 4, y, x]
        cos = np.cos(angle)
        sin = np.sin(angle)
        h = geometry[0, 0, y, x] + geometry[0, 2, y, x]
        w = geometry[0, 1, y, x] + geometry[0, 3, y, x]
        endX = int(offsetX + (cos * geometry[0, 1, y, x]) + (sin * geometry[0, 2, y, x]))
        endY = int(offsetY - (sin * geometry[0, 1, y, x]) + (cos * geometry[0, 2, y, x]))
        startX = int(endX - w)
        startY = int(endY - h)
        boxes.append((startX, startY, endX, endY))

print(len(boxes))

A A float value representing the average confidence score of all detections.

B A list of strings containing the detected text content.

C A 2D array of pixel intensities of the input image.

D An integer representing the number of detected text boxes with confidence above 0.5.

💡 Hint

Look at what is appended to boxes and what is printed.

The code collects bounding box coordinates for detected text regions with confidence above 0.5 and prints how many such boxes were found.

❓ Model Choice

advanced

Choosing a Model Architecture for Text Detection

You want to detect text in natural scene images with varying fonts and orientations. Which model architecture is most suitable?

A A simple feedforward neural network that classifies image patches as text or non-text.

B A Generative Adversarial Network (GAN) trained to generate synthetic text images.

C A Convolutional Neural Network (CNN) based EAST detector that outputs rotated bounding boxes.

D A Recurrent Neural Network (RNN) designed for sequence prediction on text strings.

💡 Hint

Consider which model can handle spatial features and rotations for detection.

The EAST detector uses CNNs to detect text regions including rotated boxes, making it suitable for natural scenes with varied text orientations.

❓ Metrics

advanced

Evaluating Text Detection Performance

Which metric is most appropriate to evaluate the quality of text detection bounding boxes compared to ground truth boxes?

A Intersection over Union (IoU) between predicted and ground truth boxes.

B Mean Squared Error (MSE) between pixel intensities inside detected boxes.

C Accuracy of character recognition inside detected text regions.

D Perplexity score of the recognized text sequences.

💡 Hint

Think about how to measure overlap between predicted and actual boxes.

IoU measures how well predicted bounding boxes overlap with ground truth boxes, which is key for detection tasks.

🔧 Debug

expert

Debugging Text Detection Output

You run a text detection model on an image but get zero detected boxes, even though the image clearly contains text. Which of the following is the most likely cause?

A The confidence threshold is set too high, filtering out all detections.

B The model weights file is missing, so the model cannot run.

C The input image is in grayscale instead of color, causing model failure.

D The detected boxes are returned but not printed due to a missing print statement.

💡 Hint

Consider what happens if the threshold for detection confidence is too strict.

If the confidence threshold is too high, the model may discard all detections, resulting in zero boxes.