A Survey of Text Detection Algorithms in Images Based on Deep Learning
Linna Li, Cuicui Hu, Ying Liu · 2022 4th International Conference on Natural Language Processing (ICNLP) · 2022
Nowadays, text detection has infiltrated various industries like banking, education, criminal investigation, network public opinion, and more. However, the traditional way of text detection is largely dependent on the characteristics of manual design, which is time-consuming, laborious and inaccuracy. With the emergence of deep learning technology, text detection has already been transformed from text detection in static documents to more practical text detection in natural scenes. To address such challenges as complex background, improper acquisition, and multilingual text in natural scene images, there have been various improved neural network structures constructed. Especially, the performance of text detection in various scenes has been significantly improved. In learning in different scenes are summarized in detail. According to the different text objects in detection, the existing methods are classified into two categories as top-down and bottom-up, and the logic structure, advantages and disadvantages of various algorithms are summarized. In addition, the commonly used data sets and performance evaluation indexes are analyzed and explained, the practical application of text detection in different fields is introduced, and the future trend of research and development are indicated.