Enhancing crane and gate OCR efficiency at container terminal using a hybrid genetic algorithm and neural network model: case study of tangier med port

Abstract

Efficiency of container terminals is important for the modern global economy, with the need for automated systems to facilitate cargo handling and logistics management. Optical Character Recognition (OCR) plays a key role in identifying and tracking containers, reducing the possibility of human error and streamlining operations. But OCR technology is plagued with persistent issues, such as environmental factors (poor lighting, bad weather effects, container shape…), text recognition challenges (skewed text, motion blur, segmentation failure), and hardware reliability concerns (camera malfunction, network instability, and delays in real-time processing). These constraints make the errors of OCR vary between 10 and 30%, affecting the efficiency of the terminals and necessitating higher manual interventions. To overcome such challenges, this study explores the application of Genetic Algorithms (GA) as an optimization technique to enhance OCR performance in container terminals. GA-based optimization allows for adaptive learning, parameterization, and real-time adaptation, significantly improving OCR accuracy, reducing error rates, and optimizing system uptime. A mathematical framework comprising OCR system parameters (hardware, software, environment, and operational constraints) was developed to optimize uptime, accuracy, and cost-effectiveness. The research incorporates a hybrid GA-Neural Network (NN) model further, using machine learning to speed up fitness evaluation and provide optimal settings for OCR performance improvement that is applicable in real world. Clinical trial number: not applicable.