AnnoLab

High-quality document AI depends on high-quality OCR, but some of the best available OCR solutions fail to recognize rotated text within documents. We’ve developed an accurate OCR solution to capture text of all rotations, allowing our AI to extract often-missed information. #ml

Real world documents are often messy, with inconsistently rotated text and poor image quality. Contact us to learn how our latest vision+text model achieved 95% accuracy in the extraction of "File Date Stamps" like the ones below - annolab.ai

Experiment with some of the latest and greatest machine learning models on your own data. No programming required Bring your own model, or utilize one from the public domain Visit app.annolab.ai/signup to demo or contact info@annolab.ai for more info #ml #datascience

Collaboration is key to scaling an AI project, so we endeavored to make it as simple as possible in our latest AnnoLab release With our new collaborators feature you can easily add new users to a project and precisely provision their permissions with a few clicks #ai #nlproc

At AnnoLab, we want users to see our software as an extension of their own ML infrastructure. We take the stance that every action in our interface should have an equal or better equivalent in our API. Read more about our API here docs.annolab.ai #ml #nlp #ai

We call them layers, but you might think of them as branches. With layers you can safely sandbox changes in training data, memorialize model inferences, and enable human-in-the-loop workflows. Worry less, experiment more, build better models #ai #MachineLearning

When ML annotation becomes a team project, it's critical to record the status of individual annotations. That's why AnnoLab is introducing a "Review" status that can be applied at the annotation level