# AI and image search: a duo for the future Press release of 20 March 2024: How enterprise search with AI object recognition, semantic search, gallery view and OCR improves image search within companies. *Vienna, 20 March 2024* How companies benefit from smart search technology. **(Vienna, 20 March 2024)** It is already well known that companies are confronted with an ever-growing flood of data. Sectors such as media companies, archives, retail, tourism, construction and many more also have to manage a large number of images in addition to documents and emails. These usually first have to be laboriously tagged with keywords so that they can be found again quickly. Many a person longs for a Google image search directly on the network drive. The solution to the effort of managing large image archives can lie in an internal company search engine. The following article sets out the possibilities, opportunities and technological must-haves showing how, thanks to the combination of enterprise search software and new AI functionalities, internal image search is now also becoming faster, more visual and more intuitive. “Searching the complex data landscapes in companies offers a wide range of applications for artificial intelligence,” explains Christoph Wendl, expert in enterprise search solutions and CEO of Iphos IT Solutions. “AI supports companies in the efficient analysis of indexed files and helps to extract relevant information quickly. In addition to processing texts, we use machine learning to evaluate visual data.” A good enterprise search system recognises objects such as people, vehicles or technical devices in images and thus makes them easy to find using search terms. Users benefit from intuitive handling and easier access to image files. Artificial intelligence is therefore a valuable addition to internal company search. The reasons for this lie in the special features of enterprise search software. ## Central, secure access to all company systems Graphics, technical drawings, photos and even Photoshop files – different file formats and security levels often mean that image files are distributed across different systems and storage locations within the company. This fragmentation makes a consistent and efficient search more difficult. Software such as enterprise search solutions can build a bridge between all data sources here: “An efficient image search must fulfil the function of opening up and organising files from all relevant storage locations,” explains Wendl. “This comprehensive data pool is fundamental to being able to apply AI functionalities meaningfully to all internal company data. Compliance with data protection aspects is integrated into the enterprise search software: by incorporating existing authorisation systems, users can only see what they are allowed to see.” ## Semantic search for objects in images The biggest shortcoming of manual image search is the file names. Unless someone has helped out by hand with considerable effort, you are faced with a mass of images with cryptic letter-and-number combinations that say nothing about the content of the image. Searching these image archives thus becomes a lengthy and tedious procedure. Implementing semantic image search can remedy this. It attempts to interpret and index images on the basis of meanings and concepts, instead of relying only on file names or keywords that were assigned to the images manually. “A good internal company search can be extended with object recognition and context recognition technologies,” Wendl reports on the AI-supported approach. “Topics, concepts and objects are recognised in the images and indexed as potential search terms. This considerably improves the accuracy and relevance of the results and makes the search experience largely intuitive. If users need, for example, an advertising graphic showing a computer, they only have to enter the term ‘PC’ and select the right one from all the recognised computer images.” In their search, employees are no longer dependent solely on metadata and meticulous naming standards. Rather, all available image material can be searched precisely for the desired image content. As a rule, good enterprise search solutions should also include synonym recognition. This means that PCs recognised in images can also be found when the user searches not for “PC” but for synonymous terms such as “computer”, “work station” or “laptop”. Such combinations of technologies in search increase the benefit of enterprise search in image search enormously. ## Automatic categorisation thanks to artificial intelligence “Thanks to enterprise search technologies, tediously clicking through file paths that lead into deep folder structures is a thing of the past. Keywords for files can be assigned automatically with the help of our AI’s text recognition and object recognition algorithms. Images are classified automatically in the process, which in turn promotes organised data management and increases productivity when handling image files,” says Wendl. ## User-friendly gallery view with thumbnails When searching for files, too, the rule is: image before text. The brain processes visual information many times faster than plain text. A pictorial representation of files therefore lays the foundation for an intuitive search experience. “After the automatic ranking by relevance, one of our most important functions in image search is the gallery view with thumbnails,” explains Wendl. “Special parsers and optical character recognition allow us to offer a reduced-size preview of image files directly in the results display. This also includes formats that cannot be displayed as images in Windows Explorer, such as Photoshop files.” Here, new technologies are combined with user-oriented presentation that allows more efficient handling of visual resources. ## Character recognition to increase efficiency in the company Enterprise search has always relied on integrating new technologies to make internal company searching more intelligent and intuitive. Another good example of the successful use of technology in internal search engines is optical character recognition (OCR). OCR makes it possible to recognise machine-readable text in the pixel information of images, graphics and scans – as is the case, for example, with scans of invoices, contracts or archived documents. Often, such documents only become findable at all in this way. It is therefore advisable for medium-sized and larger companies to integrate OCR functionality into their company search. “It is precisely the intelligent combination of object recognition in the image and OCR text recognition that can in turn lead to synergies for a company,” says Christoph Wendl. “For example, it is possible to read the number plates of cars recognised in images using OCR, or to recognise brand names in product images.” More information on enterprise search with searchit: [www.searchit-enterprise-search.com](https://www.searchit-enterprise-search.com) ## About searchit The international company Iphos IT Solutions is redefining “enterprise search engines” for businesses. With the launch of searchit, the company is positioning itself as a sustainable innovator in the industry. With numerous AI functions, such as AI-supported image search, searchit is setting new standards in the industry. The in-house AI Lab works on highly innovative AI applications in search through the processing and generation of images and texts. Ing. Christoph Wendl, together with Lyubomir Ivanov, leads the company, founded in 1998, as Chief Executive Officer (CEO); the company meets the current challenges of IT with innovative solutions. ## Media contact - Ing. Christoph Wendl, Managing Director, Iphos IT Solutions GmbH - Khekgasse 35, 1230 Vienna, Austria - Phone: +43 1 869 84 00 - Email: [info@searchit.cc](mailto:info@searchit.cc) - [Release as PDF (German)](https://searchit-agent.ai/pr/ki-und-bildersuche-ein-duo-fuer-die-zukunft/presseaussendung-ki-und-bildersuche-duo-fuer-die-zukunft.pdf) --- - searchit AI Agent: This version is generated from the page's language files (scripts/gen-unternehmen.py). The page itself is authoritative.