UI Component Detection for IBM AS400 System

🏢 The Challenge#

Legacy IBM AS400 systems present critical modernization barriers:

• 📺 Outdated Interfaces: Text-based UIs from decades past resist modern integration
• 🔧 Manual Bottlenecks: Component identification requires extensive human intervention
• ❌ Error-Prone Process: Human oversight leads to inconsistent automation results
• ⚡ Integration Friction: Legacy systems can't connect seamlessly with modern applications
• 📈 Scalability Issues: Manual approaches don't scale across enterprise AS400 deployments

🔬 My Solution#

Developed an AI-powered computer vision pipeline that automatically detects and maps AS400 UI components:

🖼️ Advanced Image Processing

• OpenCV Pipeline → Sophisticated preprocessing and noise reduction
• CVAT Integration → Professional annotation tools for training data creation
• Custom Augmentation → Specialized techniques for legacy interface variations
• Noise Resilience → 85% accuracy even in challenging legacy environments

🧠 Cutting-Edge ML Architecture

• CNN Models → Deep feature extraction using TensorFlow/PyTorch
• DETR Integration → Facebook's Detection Transformer for advanced object detection
• Transfer Learning → Leveraging pre-trained models for faster convergence
• Custom Loss Functions → Optimized for UI component classification challenges

Architecture Overview

The UI detection system follows a comprehensive pipeline architecture that transforms legacy AS400 screenshots into actionable UI component data:


AS400 Legacy Interface
Screenshots & text-based UI
OpenCV Preprocessing
Noise reduction & image enhancement
CVAT Annotation
Training data creation
Data Augmentation
Custom techniques for legacy UI
Machine Learning Models
CNN & DETR processing
CNN Feature Extraction
TensorFlow/PyTorch models
DETR Object Detection
Facebook Detection Transformer
UI Component Classification
85% accuracy in noisy interfaces
Integration APIs
Modern web app connectivity
Automated Mapping
Documentation generation
Modernization Pipeline
60% reduction in manual effort
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Image Processing Pipeline

Implemented advanced preprocessing using OpenCV
Developed noise reduction algorithms for legacy interfaces
Used CVAT (Computer Vision Annotation Tool) to annotate AS400 interface images for training data
Created custom data augmentation techniques for improved model performance

Machine Learning Models

Built and trained CNN models using TensorFlow/PyTorch
Implemented DETR (Detection Transformer) from Facebook for advanced object detection
Implemented transfer learning for better accuracy
Developed custom loss functions for UI component classification

Integration Framework

Created seamless integration APIs for modern web applications
Built real-time component detection system
Implemented automated mapping and documentation generation

Legacy Interface Complexity

The main challenge was dealing with the complexity and inconsistency of legacy AS400 interfaces. These applications are decades old and extremely difficult to integrate with modern systems. We solved this by:

Developing robust preprocessing algorithms
Creating comprehensive training datasets
Implementing adaptive detection mechanisms

Accuracy in Noisy Environments

Achieved 85% accuracy in noisy legacy interfaces through:

Custom data augmentation techniques
Continuous learning and model refinement

Results & Impact#

This project successfully modernized the approach to legacy system integration:

85% accuracy in automated UI component recognition
60% reduction in manual identification efforts
Enhanced system usability through better integration capabilities

The solution has become a cornerstone tool for legacy system modernization initiatives.