The use of digital images has become more and more frequent in several areas. The modern world is full of text, which humans use to identify objects, navigate and make decisions. Although the problem of text recognition has been extensively studied within certain domains, detecting and recognizing text in identification documents remains an open challenge. We present an architecture that integrates the different localization, extraction and recognition algorithms applied to extracting text in generic identification documents. The proposed localization method uses the MSER algorithm together to contrast enhance and edge detection to find the possible characters. The selection stage was developed through the search for heuristics, capable of classifying the located regions in textual and non-textual. In the recognition step, an iterative method is proposed to improve OCR performance. The process was evaluated using the metrics precision and recall and a proof of concept of the system was performed in a real environment. The proposed approach is robust in detecting texts from complex images with different orientations, dimensions and colors. The text recognition system presents competitive results, both in accuracy and recognition rate, when compared with other systems in the current technical literature. Showing excellent performance and feasibility of its implementation in real systems.
Many studies in the field of robot navigation have focused on environment representation and localization. The goal of map representation is to summarize spatial information in topological and geometrical abstracts. By providing strong priors, maps improve the performance and reliability of automated robots. Due to the transition to fully automated driving in recent years, there has been a constant effort to design methods and technologies to improve the precision of road participants and the environment's information. Among these efforts is the High Definition (HD) Map concept. Making HD maps requires accuracy, completeness, verifiability, and extensibility. Because of the complexity of HD mapping, it is currently expensive and difficult to implement, particularly in an urban environment. In an urban traffic system, the road model is at least a map with sets of roads, lanes, and lane markers. While more research is being dedicated to mapping and localization, a comprehensive review of the various types of map representation is still required. This paper presents a brief overview of map representation, followed by a detailed literature review of HD Map for automated vehicles. The current state of AV mapping is encouraging, the field has matured to a point where detailed maps of complex environments are built in real-time and have been proved useful. Many existing techniques are robust to noise and can cope with a large range of environments. Nevertheless, there are still open problems for future research. AV mapping will continue to be a highly active research area essential to the goal of achieving full autonomy.
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