Abstract
With the rapid development of computer science, a large number of images and an explosive amount of information make it difficult to filter and effectively extract information. This article focuses on the inability of effective detection and recognition of English text content to conduct research, which is useful for improving the application of intelligent analysis significance. This paper studies how to improve the neural network model to improve the efficiency of image text detection and recognition under complex background. The main research work is as follows: (1) An improved CTPN multidirectional text detection algorithm is proposed, and the algorithm is applied to the multidirectional text detection and recognition system. It uses the multiangle rotation of the image to be detected, then fuses the candidate text boxes detected by the CTPN network, and uses the fusion strategy to find the best area of the text. This algorithm solves the problem that the CTPN network can only detect the text in the approximate horizontal direction. (2) An improved CRNN text recognition algorithm is proposed. The algorithm is based on CRNN and combines traditional text features and depth features at the same time, making it possible to recognize occluded text. The algorithm was tested on the IC13 and SVT data sets. Compared with the CRNN algorithm, the recognition accuracy has been improved, and the detection and recognition accuracy has increased by 0.065. This paper verifies the effectiveness of the improved algorithm model on multiple data sets, which can effectively detect various English texts, and greatly improves the detection and recognition performance of the original algorithm.
Highlights
With the rise and popularity of the Internet of ings, there will be a huge amount of data every day, these data with the development and change of society
In order to solve the problem of multidirectional text detection, the SegLink [1] algorithm cuts the text into smaller text blocks that are easier to detect and connects the small text blocks into complete text areas
TextBoxes [2] algorithm, with SSD as the basic framework, adjusts the text area candidate box’s length and width ratio and convolution core into rectangles, proposing an end-to-end text detector, so that it is more suitable for detecting slender lines of text
Summary
With the rise and popularity of the Internet of ings, there will be a huge amount of data every day, these data with the development and change of society. In order to solve the problem of multidirectional text detection, the SegLink [1] algorithm cuts the text into smaller text blocks that are easier to detect and connects the small text blocks into complete text areas. Mask TextSpotter [3,4,5] algorithm, in order to solve the problem of text that can detect any shape, combines FPN network, Fast RCNN network, and RPN network and introduces the idea of segmentation to propose an end-to-end text detection and recognition algorithm. With the help of high-performance computing platforms and large-scale data sets, methods based on deep learning have made great breakthroughs in the field of computer vision in recent years and are currently the main technical direction for studying text recognition problems from all walks of life. Scientific Programming network to achieve effective overcoming of interference with natural scenes and to detect and recognize scene text
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