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.

使用深度学习和光学字符识别技术，本研究提出了一种通用系统，可以从给定的输入图像中数字化相关的地图组件数据，并创建一个格式化的文本文件。使用 Faster-RCNN 目标检测模型可以获得最高的平均精度和最高的 F1 分数值，分别为 0.68 和 0.76。通过实验结果证明，在将包含文本的图像发送到复杂的预处理流水线中去除畸变时，可以通过 OCR 来改进结果。