We investigate different approaches to the recognition of electromagnetic showers in the data which was collected by the international collaboration OPERA. The experiment initially was designed to detect neutrino oscillations, but the data collected can also be used for the development of the machine learning techniques for electromagnetic shower detection in photo emulsion films. Such showers may be used as signals of Dark Matter interaction. Due to the design of the detector and exposure time, emulsion films contain few million of traces of cosmic rays and around 1000 signal tracks attributed to single shower. We propose three different algorithms for the shower identification. All the algorithms achieve higher performance than baseline and can completely clean the detector volume from the background tracks saving about a half of the signal tracks.