This paper is devoted to the efficiency analysis of detectors for the recognition of digits, which are mechanically stamped by a special machine on a steel cast billet. Such detectors are based on pre-trained deep neural networks. In the study, we analyze the performance of four different Faster R-CNN-based detectors. These neural networks have been trained and tested on our own training dataset obtained from the electro-metallurgical plant. According to the experiments, the best results are achieved by the Faster-RCNN Inception-Resnet v2 neural network detector. Its accuracy is about 98% on the test set.