# Optical High-Speed Rolling Mark Detection Using Object Detection and Levenshtein Distance
## 4 Example Evaluation and Results
### Results of the Parser and Rolling Mark Correction and Application Scenarios  
Defining a metric to determine whether the input data (detections from the neural network) have been correctly corrected or parsed is not a trivial task. Various factors can complicate the process of \"correct correction\" or even render it impossible. For instance, if a significant portion of a rolling mark is not visible, as illustrated in Figure 9, achieving a precise correction becomes challenging. Furthermore, attempting to establish a specific criterion, such as \"the maximum allowable number of incorrectly detected characters\", in  
Figure 6: Detection results using only test data (i.e., images that were not used in the training process) showing high recognition accuracy.  
order to deduce the correct rolling mark is impractical due to the diverse and similar nature of rolling marks. A universal answer cannot be provided.  
Moreover, it is important to note that without any correction, the input data already exhibit high quality with minimal errors. The need for correction primarily arises in exceptional cases. Consequently, the role of the parser often revolves around exclusive parsing rather than extensive correction. In general, it has been observed that the necessity for correction diminishes as the input data quality improves.  
In light of these considerations, this section aims to provide insights into the expected quality of the results by showcasing example images and highlighting scenarios in which erroneous outcomes may arise.  
It is feasible to generate different representations of rolling marks automatically. In Figure 7, each distinct rolling mark is assigned a defined color. In Figure 8, the color selection is based on the steel grade. However, it is also possible to filter for other information contained within the rolling marks. For instance, one can filter for rails from a specific manufacturer with a designated year of manufacture.  
By examining Figures 7 and 8, certain assumptions can be made: Initially, there were only two rails in the depicted area, represented by the light blue and green lines. Subsequently, a track with a higher steel grade (yellow line) was inserted. However, this track required multiple corrections, as indicated by the red, blue, and purple lines. It is possible that this track belongs to a batch with quality issues and may need to be replaced.  
The light blue and green lines correspond to relatively recent tracks manufactured in 2011 and 2015, respectively, while the yellow line represents a much older track produced in 1998. The rails used for track correction (red, blue, and purple lines) are also younger, originating from the years 2010, 2014, and 2019. It is advisable for a track infrastructure operator to pay close attention to the track from 1998 (yellow line) during the next maintenance cycle, as further repairs may be necessary.  
In Figure 9, the indication of the production month (originally September, represented by the Roman numer IX) is obscured by a ground wire. Consequently, after parsing, the production month was incorrectly identified as January (represented by the Roman numeral I). Such \"simple errors\" can be automatically corrected by a multi-step correction  
Figure 8: Representation of various rolling marks. Each color represents a different steel quality. Blue Line: quality R260; Yellow Line: quality R350 HT.  
Figure 7: Representation of various rolling marks. Each color represents a different rolling mark.  
procedure. In this process, all rolling marks that are located between two rail joints (welds, connecting lugs) are combined, and an additional correction process is performed. It is important to note that this analysis is based on test data only. None of the images were previously processed by the neural network.