How to Use Document AI from Vertex AI | Get data precision and labels from documents in structure
TLDRThis video tutorial showcases the power of Document AI in the Google Cloud environment, demonstrating how to transform unstructured data into actionable information. By using pre-trained models or creating custom ones, users can extract text and values from various documents like invoices, bank statements, and receipts. The process involves enabling the API, configuring datasets, importing documents, and training the AI to recognize and label data accurately. The end result is a trained model that can streamline data extraction for businesses, with the option to deploy the model for further integration into their systems.
Takeaways
- 📚 The video discusses using AI to structure documents and convert dark data into actionable business data.
- 🔍 Google Cloud's Document AI is introduced as a tool for this purpose, with a focus on its ability to analyze various types of documents.
- 📈 The process involves logging into Google Cloud, finding Document AI, and understanding the pricing model based on the number of pages processed.
- 📋 Pre-built processes are available for different document types, such as invoices, bank statements, driver's licenses, and passports.
- 📝 Document AI can extract information from images and present it in a human-readable or machine-learnable format.
- 📁 The video demonstrates how to create a custom processor for expense receipts, including setting up a dataset and importing documents.
- 📂 The user is guided through the steps of enabling the API, configuring the dataset, and importing documents into a Google Storage bucket.
- 🏗️ The process of training the AI involves splitting the data into a training set and a test set, with 80% for training and 20% for testing.
- 📊 After training, the AI model can be evaluated and tested to ensure its accuracy in extracting information from documents.
- 🔧 The video mentions the possibility of deploying the trained model for use in a business environment, with sample requests provided for developers.
- 📌 The video concludes by encouraging viewers to subscribe for more content and hints at future tutorials.
Q & A
What is the primary purpose of using AI for document processing as described in the script?
-The primary purpose is to transform dark data into powerful, structured data that can be used in business applications by extracting information from various documents like invoices, bank statements, driver's licenses, and receipts.
How does the AI process documents in the Google Cloud environment?
-The AI processes documents by enabling the Document AI API, analyzing documents, and extracting text and values from them. It can handle a variety of document types and can be trained to recognize specific data within those documents.
The pre-built processes include extracting information from bank statements, pay slips, invoices, claim forms, driver's licenses, and passports.
-null
How does Document AI handle the cost of processing documents?
-The cost is based on the number of pages processed, with a specific amount per 1,000 pages. Users can check the pricing details to understand the costs associated with their document processing needs.
What is the role of Auto Labeling in Document AI?
-Auto Labeling attempts to automatically identify and label information such as supplier names, receiver names, and other details within the documents, making the data extraction process more efficient.
How does one train a custom version of the Document AI processor?
-To train a custom version, users need to import their documents, configure a dataset, and perform data labeling. They then split the data into training and test sets, and run an evaluation to create a trained model.
What is the evaluation process in Document AI?
-The evaluation process involves running the trained model on a test set of documents to assess its accuracy and performance. This helps users understand how well the AI can extract and label information from documents.
How can the trained Document AI model be integrated into a business environment?
-Once trained, the model can be deployed and integrated into the business environment. Developers can use the provided sample requests to pass documents through the model, which will then return the extracted data.
What are the benefits of using Document AI for invoice processing?
-Document AI simplifies the process of extracting information from invoices, such as dates, supplier names, total amounts, and currency, which would otherwise require manual data entry. This saves time and reduces errors.
How does Document AI handle multiple invoices from different suppliers?
-Document AI can be trained with invoices from multiple suppliers, allowing it to learn and recognize different data formats. This enables the AI to extract the necessary information accurately for various entities.
Outlines
📚 Introducing Document AI for Structured Data
The video begins by discussing the use of AI to structure documents, transforming dark data into actionable information for business applications. The focus is on using Google Cloud's Document AI to analyze various documents like invoices, bank statements, driver's licenses, and receipts. The tutorial aims to guide users through setting up Document AI, exploring pre-trained processes, and understanding the costs associated with the service.
📁 Uploading and Processing Documents
The second paragraph explains the process of uploading documents into Google Cloud Storage and creating a new bucket for receipts. It details the steps to import documents into Document AI, enabling auto-labeling to identify supplier names, receiver information, and other details from receipts. The paragraph also covers the importance of splitting the data into training and test sets for model evaluation and training.
🔍 Evaluating and Deploying the AI Model
In the final paragraph, the video script describes how to evaluate the trained AI model by running an evaluation and reviewing the results. It emphasizes the ease of deploying the model into the user's environment by downloading metrics and using sample requests. The script concludes by encouraging viewers to subscribe for more content and hints at the potential of using Document AI for various entities and invoices.
Mindmap
Keywords
💡AI
💡Vertex
💡Dark Data
💡Google Cloud
💡Document AI
💡Data Structuring
💡API
💡Data Labeling
💡Training Set
💡Evaluation
💡Deployment
Highlights
Using AI to structure documents and convert dark data into powerful, usable data for business applications.
Exploring Document AI in the Google Cloud environment for processing various types of documents like invoices, bank statements, driver's licenses, and receipts.
Creating custom processes for document analysis using pre-trained models.
Enabling the Document AI API and understanding the cost structure based on the number of pages processed.
Utilizing pre-built processes for extracting information from bank statements, pay slips, and other financial documents.
Training custom processors to extract text and values from receipts, such as date, supplier name, total amount, and currency.
Configuring datasets and automating the creation of Google Storage buckets for document storage.
Importing documents into the system and enabling auto-labeling to identify key information in the documents.
Splitting the data into training (80%) and test (20%) sets for model evaluation.
Training the processor to recognize and extract information from different types of documents.
Evaluating the trained model's performance by uploading test documents and reviewing the results.
Downloading metrics and integrating the trained model into the business environment for downstream processing.
Deploying the model for use and obtaining sample requests to integrate with existing systems.
The simplicity and ease of using Document AI to build AI solutions for document processing.
The potential for AI to streamline and automate the process of data entry from invoices and receipts.
The ability to train models on multiple suppliers' documents to create a comprehensive data extraction system.