![]() ![]() Modern society has slowly entered the era of information technology. If we only rely on human eyes to capture the complex text for extraction and analysis, manpower and time are highly consumed. In today’s real social life, people have long been surrounded by text images with rich semantic information. Unlike society in the old era, the presentation of text is mostly saved and transmitted by paper materials. The information transmitted by text breaks through the limits of space and time. Text is the cornerstone of human civilization, the crystallization of human wisdom, is a symbol to record human practice in social life, plays an indispensable role in the transmission of information, and is an important link connecting the world. The rich text information in the images of specific scenes is of great significance for the understanding of the scene. Text recognition, based on natural scenes, has been one of the hottest research projects in recent years. Through experiments on text data sets of natural scenes, the accuracy of this method reached 93.87%, which is nearly 0.96–1.02% higher than that of traditional methods, and which proves the feasibility of this method. Finally, we use the efficient deep learning network (EE-ACNN), which combines a convolutional neural network (CNN) with an end-to-end algorithm and multi-scale attention to enrich the text features to be detected, expands its receptive field, produces good robustness to the effective natural text information, and improves the recognition performance. In addition, we integrate a multi-scale attention mechanism to obtain attention features of different scale feature maps. First, the task of text detection and recognition is completed in an end-to-end way in a framework, which can reduce the cumulative error prediction and calculation caused by cascading, and has higher real-time and faster speed. To solve this problem, we propose a text recognition algorithm based on natural scenes. The problems of high error detection rate and low recognition accuracy have brought great challenges to the task of text recognition. However, text images in natural scenes often contain a lot of noise data, which leads to error detection. ![]() Text recognition in natural scenes has been a very challenging task in recent years, and rich text semantic information is of great significance for the understanding of a scene. ![]()
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