Document layout analysis in Computer Vision - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Understanding Document Layout Analysis

Which of the following best describes the main goal of document layout analysis in computer vision?

A To classify documents into categories such as invoices, letters, or forms.

B To identify and segment different structural components like text blocks, images, and tables within a document image.

C To enhance the resolution of scanned document images for better readability.

D To translate handwritten text into digital text using optical character recognition.

💡 Hint

Think about what parts of a document you want to separate before reading the text.

Document layout analysis focuses on detecting and segmenting different regions like paragraphs, images, and tables to understand the document's structure.

❓ Predict Output

intermediate

Output of a Simple Layout Segmentation Code

What is the output of the following Python code snippet using OpenCV for detecting contours in a document image?

Computer Vision

import cv2
import numpy as np

# Create a blank white image
img = np.ones((100, 100), dtype=np.uint8) * 255

# Draw two black rectangles simulating text blocks
cv2.rectangle(img, (10, 10), (40, 40), 0, -1)
cv2.rectangle(img, (60, 60), (90, 90), 0, -1)

# Threshold the image
_, thresh = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY_INV)

# Find contours
contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

print(len(contours))

A 2

B 1

C 0

D 4

💡 Hint

Each black rectangle should be detected as one contour.

The code draws two black rectangles on a white background, then inverts the image and finds contours. Each rectangle forms one contour, so the output is 2.

❓ Model Choice

advanced

Choosing a Model for Document Layout Analysis

You want to build a model that detects and classifies regions like paragraphs, titles, tables, and figures in scanned documents. Which model architecture is most suitable?

A A recurrent neural network (RNN) for sequence prediction.

B A convolutional neural network (CNN) for image classification only.

C A region-based convolutional neural network (R-CNN) for object detection and segmentation.

D A generative adversarial network (GAN) for image generation.

💡 Hint

Think about models that can locate and classify multiple objects in an image.

R-CNN models are designed to detect and segment multiple objects or regions in images, making them suitable for document layout analysis.

❓ Metrics

advanced

Evaluating Document Layout Segmentation

Which metric is most appropriate to evaluate the accuracy of detected layout regions compared to ground truth regions?

A Intersection over Union (IoU)

B Mean Squared Error (MSE)

C Accuracy of text transcription

D Perplexity

💡 Hint

Consider a metric that measures overlap between predicted and true regions.

IoU measures the overlap between predicted and ground truth regions, making it ideal for evaluating segmentation tasks like layout analysis.

🔧 Debug

expert

Debugging a Layout Detection Pipeline

You have a pipeline that extracts text blocks from scanned documents using thresholding and contour detection. Sometimes, it misses small text blocks. Which change is most likely to fix this issue?

A Reduce the image resolution to speed up processing.

B Use a Gaussian blur to smooth the image before thresholding.

C Increase the threshold value to make the image darker.

D Apply morphological dilation before contour detection to connect small text pixels.

💡 Hint

Think about how to connect small separated pixels to form bigger blocks.

Morphological dilation expands white regions, helping connect small text pixels into larger blocks that contours can detect.