Transfer Learning Approach for Railway Technical Map (RTM) Component Identification

TL;DR

This paper presents a transfer learning approach using deep learning models, particularly Faster-RCNN, to accurately identify and digitize railway technical map components from images, enhancing railway management systems.

Contribution

It introduces a novel system combining object detection and OCR techniques for railway map component recognition, demonstrating improved accuracy with pre-processing pipelines.

Findings

Faster-RCNN achieved the highest mAP of 0.68 and F1 score of 0.76.

Pre-processing images improves OCR accuracy.

The system effectively digitizes map data from PDFs.

Abstract

The extreme popularity over the years for railway transportation urges the necessity to maintain efficient railway management systems around the globe. Even though, at present, there exist a large collection of Computer Aided Designed Railway Technical Maps (RTMs) but available only in the portable document format (PDF). Using Deep Learning and Optical Character Recognition techniques, this research work proposes a generic system to digitize the relevant map component data from a given input image and create a formatted text file per image. Out of YOLOv3, SSD and Faster-RCNN object detection models used, Faster-RCNN yields the highest mean Average Precision (mAP) and the highest F1 score values 0.68 and 0.76 respectively. Further it is proven from the results obtained that, one can improve the results with OCR when the text containing image is being sent through a sophisticated pre-processing pipeline to remove distortions.