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.

翻译：多年来铁路运输的极度普及促使全球范围内必须维护高效的铁路管理系统。尽管目前存在大量计算机辅助设计的铁路技术图纸，但仅以便携式文档格式提供。本研究利用深度学习和光学字符识别技术，提出了一种通用系统，可从给定输入图像中数字化相关地图组件数据，并为每张图像创建格式化文本文件。在所使用的YOLOv3、SSD和Faster-RCNN目标检测模型中，Faster-RCNN分别取得了最高的平均精度均值（0.68）和最高的F1分数值（0.76）。进一步通过实验结果证明，当包含文字的图像经过复杂的预处理流程以消除畸变后，可以显著提升光学字符识别的效果。