Document AI: Introduction, processors & evaluation

Document AI: Introduction, processors & evaluation is profiled by BTW Media because published evidence links it to internet infrastructure, governance, operational dependencies, or market visibility.

• Document AI turns unstructured content into structured data making it easier to understand, analyse, and consume.
• A Document AI Processor is an interface between the document file and a Machine Learning model designed for a document-focused task.

Google Docs AI is a powerful tool that can help you create, edit, and collaborate on documents with ease. By using the built-in artificial intelligence, users can take advantage of features like automatic grammar and spelling checks, smart suggestions, and voice typing.

What is Document AI

Document AI turns unstructured content into structured data making it easier to understand, analyse, and consume. It extracts and classifies information from unstructured documents.

Its an end-to-end, cloud-based platform for Document Processing.

Along with reading and ingesting user’s documents, it also understands the spatial structure of the document. For example, if someone runs a Customer Feedback Form (Q&A type) through a parser, Document AI understands that there are questions and answers in the customer feedback form, and he’ll get those back as key-value pairs. Now as this data is structured and is available in key-value pairs, it becomes more useful for him. For ex: Users can run some quick analytics through this and understand the customer sentiment from the feedback. They can easily incorporate the output into your applications by calling an API.

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Document AI Processor functions

A Document AI Processor is an interface between the document file and a Machine Learning model designed for a document-focused task. Here are the functions of the Document AI Processor:

• OCR: Document OCR can be used to identify & extract text in different types of documents.
• Form Parsing: Form Parser can be used to extract form elements such as text and checkboxes.
• Quality Analysis: Document Quality Processor can be used for intelligent document quality processing.
• Splitting: Document Splitter can be used to identify document boundaries to split in a large file.
• Classification: For ex. Lending Doc Splitter/Classifier can be used to identify documents in a large file and classify known lending doc types.
• Entity Extraction: For ex. Invoice Parser can be used to extract 30+ fields from Invoices: Id, Amount, lineitem etc.

Evaluate processor performance

Document AI generates evaluation metrics, such as precision and recall, to help users determine the predictive performance of their processors.

These evaluation metrics are generated by comparing the entities returned by the processor (the predictions) against the annotations in the test documents.

If their processor does not have a test set, then you must first create a dataset and label the test documents.

An evaluation is automatically run whenever you train or uptrain a processor version.

Users can also manually run an evaluation. This is required to generate updated metrics after you’ve modified the test set, or if they are evaluating a pretrained processor version.

An important point to note here is that, Document AI cannot and does not calculate evaluation metrics for a label if the processor version cannot extract that label (for example, the label was disabled at the time of training) or if the test set does not include annotations for that label. Such labels are not included in aggregated metrics.