top of page
Original on transparent.png
LLM.png

LLM vs OpenCV
Choosing the Right Approach for Dynamic Image Extraction

In the era of Generative AI, enterprises are eager to deploy LLMs for various tasks, often without fully understanding their applicability and limitations. I frequently encounter developers who, when given an image analysis task, immediately turn to LLMs without first considering the nature of the image and the fundamental steps required for reliable processing. Instead of building accountable, structured code, they depend on LLM-generated solutions, which, while sometimes functional, are often neither reliable nor production-ready. This approach frequently leads to repeated corrections and evaluations, causing inefficiencies in deployment.

Image extraction is a crucial component of computer vision applications, especially when dealing with dynamically changing custom images. Traditionally, OpenCV has been the go-to library for such tasks, offering a vast suite of image processing techniques. However, with the advent of Large Language Models (LLMs) integrated with vision capabilities, a new paradigm is emerging. This article explores the differences between LLM-based approaches and OpenCV techniques for image extraction and their respective use cases.

Specific Use Case: Extracting Shapes and Flow Diagrams from Images

One common challenge in image extraction is detecting and extracting different kinds of shapes and flow diagrams from an image. These could include:

  • Basic Geometric Shapes: Circles, rectangles, triangles, polygons

  • Flowchart Elements: Decision nodes, process blocks, connectors

  • Complex Diagrams: Network diagrams, electrical circuit diagrams, process flows

For this use case, we will explore how OpenCV and LLM-based approaches handle the extraction process.

Understanding OpenCV for Shape and Diagram Extraction

OpenCV (Open-Source Computer Vision Library) is a powerful tool for classical image processing. It provides a variety of functions for:

 

  • Preprocessing: Image resizing, thresholding, filtering

  • Shape Detection: Contour detection, Hough transform for lines and circles

  • Feature Extraction: Edge detection (Canny, Sobel)

  • Connected Components Analysis: Useful for extracting flowchart elements

Strengths of OpenCV:

  • Efficient and Lightweight: Works well for real-time applications.

  • Deterministic Processing: Given a specific algorithm and parameters, results remain consistent.

  • Customizability: Fine-grained control over each processing step.

  • Low Computational Overhead: Works efficiently on edge devices.

Limitations of OpenCV:

  • Struggles with Complex Diagrams: When shapes overlap or vary in size, it may require extensive tuning.

  • Fails with Handwritten or Noisy Data: OpenCV relies on clear boundaries and structured patterns.

  • Rule-Based and Manual Tuning: Requires extensive fine-tuning for varied image inputs.

LLM-Based Approaches for Shape and Diagram Extraction

With the rise of multimodal AI, LLMs integrated with vision models (e.g., GPT-4V, BLIP, or CLIP) are proving to be powerful alternatives for extracting flow diagrams and shapes. These models leverage deep learning to understand complex visual structures dynamically.

​

How LLMs Extract Shapes and Flow Diagrams:

  • Vision-Language Models (VLMs): Convert images into meaningful text-based descriptions of their contents.

  • Context-Aware Extraction: Can recognize relationships between different shapes and their connections.

  • Few-Shot and Zero-Shot Learning: Adapt to different diagram types with minimal fine-tuning.

  • Integration with OCR: Useful for extracting textual information from flowcharts.

Strengths of LLM-Based Extraction:

  1. Handles Variability Well: Can adapt to different drawing styles and dynamic changes.

  2. Understands Higher-Level Structures: Recognizes how shapes are interconnected in a flowchart.

  3. Minimal Feature Engineering: Unlike OpenCV, requires little manual preprocessing.

  4. Scales Well with Large Datasets: Learns from diverse visual representations.

Limitations of LLM-Based Approaches:

  • Computationally Expensive: Requires significant GPU resources.

  • Latency Concerns: Not ideal for real-time applications.

  • Black-Box Nature: Harder to interpret and debug than OpenCV.

When to Use OpenCV vs. LLM-Based Approaches?

LLM2.png

Hybrid Approach: Best of Both Worlds?

Instead of choosing between OpenCV and LLMs, combining both can provide the best results. A possible hybrid pipeline for extracting shapes and flow diagrams could be:

1. Preprocessing with OpenCV: Use traditional techniques for denoising, thresholding, and initial shape detection.

2. Feature Enhancement with Deep Learning: Utilize CNNs or Transformers to refine the extracted features.

3. Semantic Understanding with LLMs: Use vision-language models to interpret and label the detected shapes and their relationships.

4. Post-Processing with OpenCV: Apply additional filtering or corrections based on LLM results.

Conclusion

Choosing between OpenCV and LLM-based approaches depends on the complexity of the task. OpenCV is ideal for real-time, rule-based shape extraction, while LLMs offer adaptability and higher-level understanding for complex flow diagrams. A hybrid approach can leverage both techniques to achieve optimal accuracy and performance in custom image extraction tasks.

​

Author: Bhavika Patel with AI assistance

​

bottom of page