The paper proposes an algorithm for segmentation of text, applied or presented in photorealistic images, characterized by a complex background. The algorithm is able to determine the exact location of image regions containing text. It implements the method for semantic segmentation of images, while the text symbols serve as detectable objects. The original images are pre-processed and fed to the input of the pre-trained convolutional neural network. The paper proposes a network architecture for text segmentation, describes the procedure for the formation of the training set, and considers the algorithm for pre-processing images, reducing the amount of processed data and simplifying the segmentation of the object "background". The network architecture is a modification of well-known ResNet network and takes into account the specifics of text character images. The convolutional neural network is implemented using CUDA parallel computing technology at the GPU. The experimental results for evaluating quality of text segmentation with IoU (Intersection over Union) criterion have proved effectiveness of the proposed method.