AI and image search: A duo for the future

How companies benefit from smart search technology

“Searching through the complex data landscapes in companies offers a diverse range of applications for artificial intelligence,” explains Christoph Wendl, expert for enterprise search solutions and CEO of Iphos IT Solutions. “AI helps companies efficiently analyze indexed files and helps extract relevant information quickly. In addition to processing texts, we use machine learning to evaluate visual data. A good enterprise search system recognizes objects such as people, vehicles, or technical devices in images and makes them easy to find using search terms. Users benefit from more intuitive handling and easier access to image files. Artificial intelligence is therefore a valuable addition to the company’s internal search. The reasons for this lie in the special features of enterprise search software.”

Centralized, secure access to all enterprise systems

From graphics, technical drawings, photos to Photoshop files – different file formats and security levels often mean that image files are spread across different systems and storage locations within the company. This fragmentation makes it difficult to search consistently and efficiently. Software such as enterprise search solutions can build a bridge between all data sources: “An efficient image search must fulfill the function of indexing and organizing files from all relevant storage locations,” explains Wendl. “This comprehensive data pool is fundamental in order to be able to apply AI functionalities to all internal company data in a meaningful way. Compliance with data protection aspects is integrated into the enterprise search software: by integrating existing authorization 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. If this was not helped by hand with a considerable amount of work, one is confronted with an abundance of images with cryptic letter-number combinations that say nothing about the image content. The search in these image archives thus becomes a lengthy and laborious procedure.
The implementation of a semantic image search can provide a remedy here. It attempts to interpret and index images based on meanings and concepts, rather than relying solely on file names or keywords that have been manually assigned to the images.

“A good internal company search can be enhanced by the technologies of object recognition and context recognition,” says Wendl of the AI-supported solution approach. “Topics, concepts and objects are recognized in the images and indexed as potential search terms. This greatly improves the accuracy and relevance of the results and makes the search experience largely intuitive. If, for example, users need an advertising graphic on which a computer is depicted, they only have to enter the term “PC” and select the correct one from all the recognized computer images.” Employees are no longer solely dependent on metadata and meticulous naming standards for search. 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 recognized in images can be found even if the user does not search for “PC”, but for synonymous terms such as “computer”, “work station”, or “laptop”. Such combinations of technologies in search increase the usefulness of enterprise search in image search enormously.

Automatic categorization thanks to artificial intelligence

“Tedious clicking through file paths that lead to deep folder structures is a thing of the past thanks to enterprise search technologies. Keywords for files can be assigned automatically using our AI’s text recognition and object recognition algorithms. Images are automatically classified, which in turn promotes organized data management and increases productivity when handling image files.

User-friendly gallery view with thumbnails

The same applies to file searches: image before text. The brain processes visual information many times faster than mere text. A pictorial representation of files thus forms the foundation for an intuitive search experience. “After the automatic ranking according to relevance, one of our most important functions in the image search is the gallery view with thumbnails,” explains Wendl. “Special parsers and optical character recognition allow us to offer a reduced preview of image files directly in the results display. This also includes formats that cannot be visualized in Windows Explorer, such as Photoshop files.” New technologies are combined with user-oriented presentation, which allows a more efficient use of visual resources.

Character recognition to increase efficiency in the company

Enterprise Search has always relied on the integration of new technologies to make internal searches smarter and more intuitive. Another good example of successful technology use in internal search engines is Optical Character Recognition (OCR). OCR allows machine-readable text to be recognized in the pixel information of images, graphics and scans – as is the case with scans of invoices, contracts, or archived writings. Often, this is what makes such documents findable in the first place. Therefore, it is recommended for medium-sized and larger companies to integrate OCR functionality into the company search. “The intelligent combination of object recognition in the image and OCR text recognition in particular can lead to synergies for a company,” says Christoph Wendl. “For example, it is possible to read the license plates of recognized cars in images using OCR, or to recognize brand names in product images.”