Pattern recognition (PR) is the study of how machines can examine the environment, learn to distinguish patterns of interest from their background, and make reliable and feasible decisions regarding the categories of the patterns. However, even after almost 70 years of research, the design of an application based on pattern recognizer remains an ambiguous goal. Moreover, currently, there are huge volumes of data that must be dealt with, which include image, video, text and web documents; DNA; microarray gene data; etc. Among the various frameworks in which pattern recognition has been traditionally formulated, the statistical and machine learning approaches have been most comprehensively studied and employed in practice. Recently, deep learning techniques and methods have been receiving increasing attention. The main objective of this review is to summarize PR applications, departing from the major algorithms used for their design. The PR approaches are subdivided into three main methods: machine learning, statistical, and deep learning. In order to evidence the multidisciplinary aspects of PR applications, attention has been focused on latest PR methods applied to five fields of research: biomedical and biology, retail, surveillance, social media intelligence, and digital cultural heritage. In this paper, we discuss in detail the recent advances of PR approaches and propose the main applications within each field. We also present challenges and benchmarks in terms of advantages and disadvantages of the selected method in each field. A wide set of examples of applications in various domains are also provided, along with the specific method applied.

模式识别（PR）是关于机器如何检查环境，学会从背景中区分出感兴趣的模式，以及就模式的类别做出可靠可行的决定的研究。但是，即使经过近70年的研究，基于模式识别器的应用程序设计仍然是一个模糊的目标。此外，当前，必须处理大量数据，包括图像，视频，文本和Web文档。脱氧核糖核酸; 基因芯片基因数据 在传统上已建立模式识别的各种框架中，统计和机器学习方法已得到最全面的研究并在实践中采用。近来，深度学习技术和方法已受到越来越多的关注。这篇综述的主要目的是总结PR应用程序，而不是用于设计的主要算法。公关方法可分为三种主要方法：机器学习，统计和深度学习。为了证明PR应用程序的多学科方面，已将注意力集中在应用于五个研究领域的最新PR方法上：生物医学和生物学，零售，监视，社交媒体情报和数字文化遗产。在本文中，我们详细讨论了PR方法的最新进展，并提出了各个领域的主要应用。我们还针对每个领域中所选方法的优缺点提出了挑战和基准。还提供了各个领域中的大量应用程序示例，以及所应用的特定方法。