At its core, UBIAI offers a powerful annotation platform that supports various data formats, including native PDFs and scanned images. One of its standout features is the integrated Optical Character Recognition (OCR) technology, which enables users to extract text from images and documents accurately. This capability is crucial for industries dealing with large volumes of unstructured data, as it allows for the efficient conversion of printed or handwritten materials into digital formats that can be easily annotated and analyzed.
The platform provides a range of tools for different types of annotations, including named entity recognition (NER), relationship extraction, and document classification. Users can label entities and relationships within texts, making it easier to train models that can understand context and semantics. UBIAI also supports multi-lingual annotation, allowing users to annotate documents in various languages, which is essential in today’s globalized work environment.
UBIAI's auto-labeling feature is another significant advantage. This functionality allows the system to pre-annotate data based on existing patterns and user-defined rules, thereby accelerating the annotation process. Users can bootstrap their projects without extensive manual input, which leads to a considerable reduction in annotation time—up to 80% in some cases. The platform also includes collaborative features that enable teams to distribute tasks effectively, track progress, and measure inter-annotator agreement to ensure high-quality annotations.
The user interface of UBIAI is designed to be intuitive and accessible, making it suitable for users with varying levels of technical expertise. The platform allows users to create projects easily, upload documents, set up annotation tasks, and start labeling with minimal setup time. Additionally, UBIAI supports multiple export formats for trained models, facilitating integration with other machine learning frameworks like SpaCy and BERT.
Key features of UBIAI include:
UBIAI serves as a robust solution for organizations looking to enhance their machine learning capabilities through efficient data labeling and model training processes. By combining advanced technology with user-friendly features, it empowers teams to produce high-quality annotated datasets that are essential for developing effective AI models across various applications.