  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.
