Linguistic Landscape (LL) research traditionally relies on manual photography and annotation of public signages to examine distribution of languages in urban space. While such methods yield valuable findings, the process is time-consuming and difficult for large study areas. This study explores the use of AI powered language detection method to automate LL analysis. Using Honolulu Chinatown as a case study, we constructed a georeferenced photo dataset of 1,449 images collected by researchers and applied AI for optical character recognition (OCR) and language classification. We also conducted manual validations for accuracy checking. This model achieved an overall accuracy of 79%. Five recurring types of mislabeling were identified, including distortion, reflection, degraded surface, graffiti, and hallucination. The analysis also reveals that the AI model treats all regions of an image equally, detecting peripheral or background texts that human interpreters typically ignore. Despite these limitations, the results demonstrate the potential of integrating AI-assisted workflows into LL research to reduce such time-consuming processes. However, due to all the limitations and mis-labels, we recognize that AI cannot be fully trusted during this process. This paper encourages a hybrid approach combining AI automation with human validation for a more reliable and efficient workflow.

Current exploration methods struggle to search for shops in unknown open-world environments due to a lack of prior knowledge and text recognition capabilities. Venue maps offer valuable information that can aid exploration planning by correlating scene signage with map data. However, the arbitrary shapes and styles of the text on signage, along with multi-view inconsistencies, pose significant challenges for accurate recognition by robots. Additionally, the discrepancies between real-world environments and venue maps hinder the incorporation of text information into planners. This paper introduces a novel signage-aware exploration system to address these challenges, enabling the robot to utilize venue maps effectively. We propose a signage understanding method that accurately detects and recognizes the text on signage using a diffusion-based text instance retrieval method combined with a 2D-to-3D semantic fusion strategy. Furthermore, we design a venue map-guided exploration-exploitation planner that balances exploration in unknown regions using a directional heuristic derived from venue maps with exploitation to get close and adjust orientation for better recognition. Experiments in large-scale shopping malls demonstrate our method's superior signage recognition accuracy and coverage efficiency, outperforming state-of-the-art scene text spotting methods and traditional exploration methods.

Digital signage is a rapidly growing industry that is transforming the way businesses communicate with their customers. In recent years, we have seen a number of trends emerge in the world of digital signage, and it is likely that we will see even more exciting developments in the coming year. One trend that is expected to continue in 2023 is the use of artificial intelligence (AI) and machine learning in digital signage. By using these technologies, businesses can personalize their digital signage content based on the specific needs and preferences of their customers. For example, a retail store could use AI to recommend products to customers based on their past purchases or browsing history.

Say goodbye to boring signage. Attention-grabbing, knock-your-socks-off immersive digital signage video content is elbowing its more staid static cousins out of the way. Video signage is increasingly pervasive and has evolved with a marked improvement in image quality, resolution, and a dynamo of a platform that supports it all. There are reasons to say that the future of #video #signage and streamed content will focus on seamless interactive personalization--whether on large screens or an individual #mobile phone. How Immersive Digital Signage Video Content Boosts Engagement Exterity is now VITEC via insight.tech

Manufacturers are finding AI is no longer the answer to automating operations and improving product quality. While AI can increase the defect detection rate by up to 90% over human inspection, it's useless if manufacturers cannot obtain the information they need when they need it. Without a faster process, they continue to run the risk of unplanned shutdowns and production errors. "The challenge manufacturers have with AI is actually validating and verifying the return of investment," says Shunichi Kagaya, Sr Engineer at Hitachi, a leader in #digital and #IoT solutions. "Manufacturers understand the value of #data and they want to use it.

When she's not writing, she works in hotel management. We increasingly use technology for a broad variety of purposes, and the more that happens, the more data we collect and store. These days, AI is transforming the way we utilize that information. Machines can read and learn from different types of data and then perform real-world tasks. That's true in almost every sector, but AI is also being used to simplify and improve how humans control crowds and populations worldwide.

This week, Reuters reported that the American drugstore chain Rite Aid has deployed facial recognition systems in 200 stores nationwide over the past eight years. And the story gets very, very hairy. Since Rite Aid refused to disclose where it used such technology, Reuters reporters took it upon themselves to visit 75 locations in the central Los Angeles metropolitan area and Manhattan. Of these, 33 had "easily recognizable" facial recognition cameras. According to Reuters, storefronts in low-income areas were almost three times as likely to have facial recognition cameras present than those in wealthier neighborhoods.

In the event of a disaster, saving human lives is of utmost importance. For developing proper evacuation procedures and guidance systems, behavioural data on how people respond during panic and stress is crucial. In the absence of real human data on building evacuation, there is a need for a crowd simulator to model egress and decision-making under uncertainty. In this paper, we propose an agent-based simulation tool, which is grounded in human cognition and decision-making, for evaluating and improving the effectiveness of building evacuation procedures and guidance systems during a disaster. Specifically, we propose a predictive agent-wayfinding framework based on information theory that is applied at intersections with variable route choices where it fuses N dynamic information sources. The proposed framework can be used to visualize trajectories and prediction results (i.e., total evacuation time, number of people evacuated) for different combinations of reinforcing or contradicting information sources (i.e., signage, crowd flow, familiarity, and spatial layout). This tool can enable designers to recreate various disaster scenarios and generate simulation data for improving the evacuation procedures and existing guidance systems.

Digital signage vendors are constantly innovating and testing new tools, ranging from AI to dynamic content to responsive touchscreens. Keeping up with these trends is absolutely key to ensure your digital signage is effective. As digital signage continually becomes more responsive and dynamic, it will become even more important to stay up-to-date on these trends to avoid falling behind the competition. The current big trends of 2019 include AI, omnichannel and personalization. In order to learn more about these trends and what's on the horizon for the future, Digital Signage Today spoke with Chris Devlin, president of digital signage software vendor Omnivex to get his perspective.

Trying to drive in a foreign land can be daunting. The car itself is pretty much the same in terms of its operational characteristics, meaning there's a steering wheel, a brake pedal, accelerator pedal and other driving-related controls that you can readily figure out. The tough part involves the act of driving the car and dealing with the differences when performing the driving task. There are likely different kinds of roadway signage than you might have seen in your home country. Fortunately, those signs are usually relatively standardized and visually easy to understand, even if you aren't especially familiar with the signage being used.