Text detection and localization in scene images: a broad review

Mahajan, Shilpa; Rani, Rajneesh

doi:10.1007/s10462-021-10000-8

Cited by 24 publications

(3 citation statements)

References 106 publications

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“…When the progress bar “centering focus” starts to load, the glass frame stops flashing. Exhibits in glass frames move to the front [ 15 ]. The specific design process is shown in Figure 4 .…”

Section: Methodsmentioning

confidence: 99%

Virtual Reality Technology of New Media Visual Simulation

2022

Computational Intelligence and Neuroscience

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In order to improve the exhibition effect of museums and expand the display ways of digital museums, a Chinese museum scene roaming application system based on the Unity 3D virtual reality engine was proposed. 3Dmax model production software was used to complete the museum scenes and the model production, material, and lighting of collections in the museum. Through the Unity 3D virtual reality engine, the design and production of the interactive system were completed. The display and application of the scene roaming system of the Chinese museum were well realized. For the system, the fast construction of virtual scenes and fixed-point triggering (FPT) optimization were carried out. The experimental results showed that the improved method was 90.5% faster than the manual coding method in the optimization of fast virtual scene construction. After using the fixed-point triggering method, the average accuracy of the original 35 scenes was 63.9%. It was concluded that the application design could simulate the museum scene well, which had a certain reference value for the application of virtual reality simulation technology in the field of cultural heritage inheritance.

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Section: Methodsmentioning

confidence: 99%

Virtual Reality Technology of New Media Visual Simulation

2022

Computational Intelligence and Neuroscience

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“…Existing text in visible images naturally includes two distinct real or synthetic types, structured or unstructured, regular or irregular texts in natural scenes, and graphical fantasy texts. The classic texts in natural scenes are the images recorded using conventional digital cameras [60]. Text localization for automatic applications such as sign and driving traffic sign recognition, reading old texts, archiving documents, finding news feeds, and recognition of the text on different locations like emergency vehicles, clothes, and billboards are fully developed with the aid of static and flying/portable robots.…”

Section: Case Studymentioning

confidence: 99%

Big Image Data 15Vs Model for Intelligent Data Ocean of Multimedia Things

Mahabadi,

Abdolazimi

2024

Preprint

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The classification of large image data using traditional methods has been based on changes in image gray features, extraction of edge and contour feature information, or conversion between image coordinate sets. However, due to the growth of new image size and big image data (BID) in real-time multimedia communication and future online big data (BD) applications, these methods have become increasingly complex and resulting in poor real-time performance. These complex methods suffer from complex algorithms, massive data communication, slow processing speed, unintelligent predictive modeling, weak data classification, limited accuracy, uncleaned usage data, combined destructive artificial and natural noise, exchanging data values over time, and exhausting updating data for accurate operation of media storage. In the era of the Internet of Multimedia Things (IoMT), most modern devices can accurately capture vast amounts of observational data in the form of valuable images, text, and acoustic recordings. This reliable data must be kept valid for local data processing at different times and for globally extracting knowledge from information at various data levels to address forthcoming challenges related to big image data. The challenges of secure communication require accurate, reliable, fast, and effective information gathering for local decision-making and global knowledge mining of BID to support cyber-physical systems for speedy data virtualization of smart city devices. Complex and uncontrollable problems persist on the outskirts of grids, fogs, and cloud networks regarding data cleansing and privacy protection for intelligent data ocean management. This study developed a new 15Vs model empirically to examine distributed big image data processing based on a modern BID connectivity approach. The model accurately extracts textured features from visible data images and overcomes a unique set of key challenges. The model introduces a strategic analyzer, uses intelligent local agents, and recruits clever global bots to provide a suitable platform for generous support of bot-oriented BD processing. This instantly forms a hierarchical data level for better data acquisition and cleansing, safe privacy protection, suitable information diffusion, and speedy knowledge extraction. Our study presents a novel definition of BID to address the ultimate challenges of data management in a high-risk environment for further big image data modeling research needs.

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“…2. Reading the nature: With the rapid growth of text extraction techniques [96], numerous real-world claims such as aiding visually challenged individuals and identifying text in the roads have emerged and been developed. We have realtime translation, medical service, navigation system for the end-user, traffic monitoring, and assistive driving systems.…”

Section: Applicationsmentioning

confidence: 99%

ETDR: An Exploratory View of Text Detection and Recognition in Images and Videos

Lokkondra¹,

Ramegowda²,

Thimmaiah³

et al. 2021

RIA

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Images and videos with text content are a direct source of information. Today, there is a high need for image and video data that can be intelligently analyzed. A growing number of researchers are focusing on text identification, making it a hot issue in machine vision research. Since this opens the way, several real-time-based applications such as text detection, localization, and tracking have become more prevalent in text analysis systems. To find out more about how text information may be extracted, have a look at our survey. This study presents a trustworthy dataset for text identification in images and videos at first. The second part of the article details the numerous text formats, both in images and video. Third, the process flow for extracting information from the text and the existing machine learning and deep learning techniques used to train the model was described. Fourth, explain assessment measures that are used to validate the model. Finally, it integrates the uses and difficulties of text extraction across a wide range of fields. Difficulties focus on the most frequent challenges faced in the actual world, such as capturing techniques, lightning, and environmental conditions. Images and videos have evolved into valuable sources of data. The text inside the images and video provides a massive quantity of facts and statistics. However, such data is not easy to access. This exploratory view provides easier and more accurate mathematical modeling and evaluation techniques to retrieve the text in image and video into an accessible form.

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Text detection and localization in scene images: a broad review

Cited by 24 publications

References 106 publications

Virtual Reality Technology of New Media Visual Simulation

Virtual Reality Technology of New Media Visual Simulation

Big Image Data 15Vs Model for Intelligent Data Ocean of Multimedia Things

ETDR: An Exploratory View of Text Detection and Recognition in Images and Videos

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