Art historians are constantly seeking new ways to organize and analyze the vast amount of information related to our artistic heritage. One potential solution is to use Artificial Intelligence (AI) tools to help manage and study museum collections.
AI-powered tools can be used to automatically tag and classify artifacts and artworks, making it easier for curators and researchers to search and access specific items in the collection.
For instance, AI can help museums gain a deeper understanding of their historical artworks by identifying patterns and trends that may not be easily discernible to humans. This can help museums develop more insightful interpretations of the past and create more engaging exhibitions and displays.
In this article, we’ll study a collection of 19th-century photographs from Japan using a combination of AI vision technologies offered by Eden AI in order to gain new insights into Japan’s history and culture.
Here are 8 potential uses and applications of AI in the study of art history.
OCR (Optical Character Recognition) allows a computer to analyze and extract text from images and scanned documents. It works by identifying the shapes of the letters, numbers, and other characters in the image. This is typically done by comparing the shapes to a predefined set of characters and using pattern recognition algorithms to determine which characters are present in the image.
In this particular reconstructed history project, it can be used to help digitize and extract text from historical documents or art:
Image object detection can be used in historical study to help identify objects in images. This can be useful for tasks such as cataloguing museum collections, analyzing historical artifacts, and studying objects in historical images and videos.
In our use case, it could be used to automatically identify the common objects that you can find in Japan of that era such as by type and material. Like in this picture :
Object detection technology can be a useful tool for historical study and museum applications, as it can help to quickly and accurately identify and classify objects in images.
Face detection technology can also be used to help identify and analyze faces in images and videos. This can be useful for tasks such as cataloging art collections, analyzing historical portraits, and studying faces in artworks and historical images and videos.
Here is an example where we used face detection with EdenAI to automatically detect faces in a historical picture, you can even analyze some emotions by the persons in the picture.
Landmark detection is a type of image detection technology that involves identifying and locating specific landmarks or features in digital images. This can be useful in a variety of applications
It can be useful in a historical context, like studying the development of a particular time period or artistic movement, or analyzing how a particular place is represented in different works.
This refers to the technology that automatically identifies and analyzes specific features or characteristics of an image like its dominant colors. It can be used to detect the alterations made to an image over time, and the analysis of the composition, color, and other visual elements of a piece of art.
Explicit content detection in historical studies and art can be a complex and sensitive topic. In some cases, historical artwork may contain explicit or offensive material that is important for understanding the context of the time period in which it was created. In such cases, it may be necessary to either provide warnings to protect vulnerable audiences or to refrain from sharing the material.
In the end, the decision on how to handle explicit content in historical art will depend on the specific context and the goals of the research or display.
Image similarity technology allows users to search for images that are similar to a given reference image as input. It uses algorithms to compare the visual characteristics of the reference image to a database of other images and returns results that match the reference image most closely by giving a similarity score.
It can be used to compare different versions of the same artwork, or to identify similarities and connections between different artworks. For example, it can determine if two paintings were created by the same artist, or to identify common elements or themes in a group of artworks.
Based on the image similarity, and trained to detect similarity between faces, face recognition compares the landmarks, features and characteristics of the face given the input reference.
It can be used to compare different portraits or other representations of the same person, or to identify similarities between the faces depicted in different artworks.
The market currently offers a variety of AI solutions that can be accessed via APIs and can efficiently process large amounts of images in the field of art and history.
Eden AI offers a platform for accessing and using a wide range of AI solutions for image processing in the fields of art and history. We strive to make it easy for our users to take advantage of these solutions by providing a single interface for accessing them.
If you have a project that involves image processing and are interested in learning more, please don't hesitate to contact us. You can also create an account and start using our platform right away.
Using computer vision technologies with Eden AI API is quick and easy.
We offer a unified API for all providers: simple and standard to use, with a quick switch between providers and access to the specificic features of each provider
The JSON output format is the same for all suppliers thanks to Eden AI's standardization work. The response elements are also standardized thanks to Eden AI's powerful matching algorithms.
With Eden AI you have the possibility to integrate a third-party platform: we can quickly develop connectors. To go further and customize your computer vision request with specific parameters, check out our documentation.