In our previous post, we explored Image OCR tools that Unitlab AI offers. In this post, we will see how we can use Image OCR for one common task: invoices.
Invoice OCR annotation is a special part of the image OCR annotation, specifically Fintech OCR annotation. Because we want to ensure that our AI-powered auto-labeling tools accurately pinpoint numbers, percentages, and currency symbols, we should use FIntech OCR, which is developed for these use cases.
In this post, we will see how to set up your project to automate your workflows regarding Invoice OCR with batch and crop auto-annotation.
Project Creation
To begin automating your Invoice OCR data annotation process, sign up for a free account on Unitlab Annotate and create a new project. During setup, select Image OCR as the annotation type and the Fintech OCR AI model as your annotation engine.
Fintech OCR is purpose-built for tasks requiring accurate data labeling, such as extracting totals, percentages, and vendor details from invoices. This data labeling tool significantly reduces manual effort and improves the precision of your dataset.
When managing projects, Unitlab Annotate also supports AI dataset management features, ensuring you can track, version, and manage your AI datasets effectively.
Use Batch Auto-Annotation
Manual image labeling for invoices, although accurate and of high quality, can be a tedious and time-consuming task, especially for large-scale projects. Unitlab Annotate’s data auto-annotation feature, specifically batch auto-annotation, solves this challenge by enabling automatic annotation of multiple invoices simultaneously with its pre-trained AI models.
Batch auto-annotation leverages the power of Fintech OCR’s auto-labeling tools to process large datasets efficiently. This image labeling solution ensures consistent and accurate results across your dataset, reducing manual intervention.
Use Crop Auto-Annotation
In cases where you only need to annotate specific parts of an invoice—such as totals, tax information, or specific line items—Unitlab Annotate’s crop auto-annotation is an invaluable feature. See it in action:
Also, if your project requires very high accuracy, you can manually label the image after using crop or batch auto-annotation provided by Unitlab Accurate. The AI model in the project auto-annotates the image, and a human data labeler can manually review or correct the annotations. This hybrid approach ensures that you can automate Invoice OCR annotation while keeping the accuracy your project requires. See the demo:
With crop auto-annotation, you can select specific areas of interest on the invoice, allowing the Fintech OCR model to apply data auto-labeling only within those areas. This focused approach ensures precise results while saving time and effort.
Integrate Your Custom AI
While Unitlab’s Fintech OCR is an excellent image annotation solution, there may be cases where you need to use a custom model tailored to your specific needs. Unitlab Annotate allows seamless integration of custom AI models into its platform, enabling you to combine the capabilities of your data labeling service with your proprietary AI.
By integrating your AI models, you can maintain control over specialized ML datasets and manage them with Unitlab’s dataset version control and AI dataset management tools. This flexibility ensures that your data annotation solution adapts to your unique requirements.
Conclusion
Unitlab Annotate’s Fintech OCR model provides a powerful data annotation service for Invoice OCR. With advanced features like batch and crop auto-annotation, this data labeling tool simplifies workflows, improves efficiency, and ensures accuracy in image labeling tasks. Whether you need a robust image labeling service for large-scale invoices or a tailored data auto-labeling solution for specific needs, Unitlab Annotate delivers.
Additionally, for those managing complex datasets, Unitlab supports comprehensive AI dataset management and dataset version control, helping you maintain organized and scalable workflows. Explore Unitlab Annotate today and discover how its auto-labeling tools can transform your Invoice OCR and data annotation processes.