A pathology laboratory processes various types of tissue and cell specimens and plays a vital role in the diagnostic process. However, pathology departments are currently facing a significant challenge due to the steady increase in incoming specimens. Increasing the workforce to match the influx is generally not feasible, so Information and Communication Technology (ICT) is seen as a potential solution. One area where ICT can be applied is in process monitoring and tracing. The increase in incoming specimens has caused queues within the laboratory, resulting in more time spent locating and retrieving individual specimens. Existing methods of tracing specimens, such as barcodes or alphabetic sorting, also require manual labor, adding further overhead. In this paper, we propose a lightweight application of optical character recognition (OCR) for specimen tracing, as part of a larger research project to optimize pathology processes at a large regional hospital in Bergen. We present a specific solution that integrates into a general process monitoring environment, and we compare different implementation techniques, particularly edge detection and neural networks. Our preliminary results indicate that this implementation can achieve an accuracy of up to 93.41%, increase sorting speed up to 54% and save up to 35% of time spent in manual sorting activities. We conclude our findings with a general discussion and outlook onto other areas where this solution could theoretically be applied.