Bynder’s Text-in-Image Search capabilities
Bynder’s Text-in-Image Search (OCR) is an advanced AI feature that extracts and indexes text within images, transforming how users discover assets in their digital libraries. By automating text detection and tagging, this feature eliminates the need for manual metadata creation, making it faster and easier to locate text-rich images, even for users unfamiliar with taxonomy or tagging. This can be especially valuable for digitized documents and images of products that contain product descriptions or ingredients.
Understanding Text-in-Image Search in digital asset management systems
Traditional DAM systems rely heavily on metadata to make assets discoverable. This dependency can become a bottleneck when metadata is incomplete or inconsistently applied. Text-in-Image Search addresses this challenge by leveraging Optical Character Recognition (OCR) to extract text from images automatically, converting it into searchable metadata. This ensures assets like product labels, branded content, or text-heavy visuals are fully searchable without requiring manual tagging, streamlining workflows, and improving accessibility.
A brief history of Text-in-Image search
Text-in-Image Search, powered by Optical Character Recognition (OCR), has advanced dramatically since its early roots in the 1910s, when machines were first developed to interpret characters. By the 1970s, innovations like Ray Kurzweil’s omni-font OCR enabled text recognition across various fonts, laying the groundwork for modern applications. The 1990s brought commercial OCR software into mainstream use, making it accessible for digitizing documents. With the rise of AI and cloud computing in the 2010s, OCR evolved further, enabling seamless integration with systems like digital asset management platforms, where it now powers robust search and tagging capabilities.
Technical foundation
Text-in-Image Search leverages Amazon Rekognition to efficiently detect and extract text from images.
- Text detection and recognition: the system scans images to identify individual characters, words, and lines. Using machine learning algorithms, it can handle multiple fonts, sizes, and orientations (up to ±90°) with high accuracy.
- Bounding and grouping: Once text is detected, the OCR engine generates bounding boxes for each word, making it easy to visualize where text resides in the image. It then groups words into meaningful lines or blocks.
- Search integration: The extracted text is indexed, allowing users to search for assets using specific keywords or phrases from the image content.
Implementation and practical applications
Text-in-Image Search automates the detection and extraction of text from images, integrating into DAM workflows. By indexing extracted text, it enables users to search for content efficiently without relying on manual tagging. Meanwhile, DAM admins would not need to spend hours enriching assets with metadata in order to make them discoverable. This feature improves asset organization, reduces manual effort, and ensures consistency across large libraries, making it a valuable tool for teams managing extensive visual content.
A common practical use case that we see among our customers is product management or extraction of product information such as ingredients, pack size, flavor, or brand name from product-related assets such as packaging artwork and pack shots. Another example would be the management of campaign assets with slogans or marketing messages. All variations of assets from a campaign with the same slogan can easily be retrieved with a simple keyword search. This capability proves invaluable for e-commerce and marketing teams because eliminates the heavy work of enriching assets with metadata while making them easy to discover.
