Recognizing text in images presents significant challenges, particularly in complex backgrounds. This technology is essential for aiding visually impaired individuals and interpreting semantic content. This survey examines various techniques developed in recent years for handling text recognition in complex images. The paper provides an analysis of related works and evaluates the performance of these recognition methods. Although image complexity is not easily defined, it can be described through parameters such as background details, noise levels, lighting conditions, textures, and fonts. Additionally, this survey discusses several benchmark datasets used in the reviewed studies. By reviewing these works, challenges in this area can be identified and compared. Complex background images limit the accuracy achieved. To improve accuracy, convolutional neural networks (CNN) are employed. The proposed method comprises three parts: the text is detected from a complex background, the text is extracted from the image using Tesseract, and all identified words are saved in a text file. An audio file is then created from the text. The proposed system reads the text from the image with the aim of providing assistance to visually impaired individuals.