Automating Document Processing with AI | Azure AI Essentials

Table of Contents

1. Introduction
2. The Importance of Document Processing
3. Optical Character Recognition (OCR)
4. Using Azure Cognitive Services for OCR
5. Form Recognizer for Document Processing
6. Layout API in Form Recognizer
7. Pre-built Models in Form Recognizer
8. Custom Models in Form Recognizer
9. Enriching Data with Azure Cognitive Search
10. Exploring Data with Azure Cognitive Search
11. Extending Azure AI Solutions

Introduction

In today's fast-paced world, organizations are dealing with a large amount of documents that need to be processed. This includes paperwork for various tasks such as buying a car, filling out tax forms, or documenting completed work. However, manual document processing is time-consuming, inefficient, and prone to human error. To overcome these challenges, organizations are turning to AI-powered solutions that can automate document processing. In this article, we will explore how You can add optical character recognition (OCR) and AI-powered search capabilities to your applications using Azure AI.

The Importance of Document Processing

Processing a large number of documents is a reality for every organization and industry. However, the manual processing of these documents is not only time-consuming but also prone to errors. Different types of forms, including internal and customer forms, may need to be handled differently. Additionally, handwritten text can be difficult to Read, further adding to the complexity of document processing. To overcome these challenges, organizations are integrating AI solutions into their workflows to automate document processing.

Optical Character Recognition (OCR)

OCR is a technology that enables the extraction of text from images and documents. With OCR, printed and handwritten text can be extracted and processed. Azure Cognitive Services provide OCR capabilities that can be easily integrated into your applications. These capabilities can be leveraged using a portal experience or through Computer Vision APIs and SDKs for various programming languages such as C#, Java, JavaScript, or Python.

Using Azure Cognitive Services for OCR

Azure Cognitive Services offer Computer Vision APIs and SDKs that allow you to add OCR capabilities to your applications. The Computer Vision Read API is specifically designed to extract text from various types of images and documents. It can extract printed text in multiple languages and handwritten text in English, including digits, currency symbols, and more. The API is optimized for text-heavy images, multi-page PDFs with mixed languages, and can even detect printed and handwritten text in the same image or document.

To use the OCR capabilities of Azure Cognitive Services, you need to make API calls by providing images or documents as input. The API supports a variety of file formats and allows you to specify page numbers or ranges for extracting text from large multi-page documents. The API will parse through the documents, use OCR to detect the text, and return a JSON response. The response will include the extracted text along with bounding box coordinates and a confidence score for each line of text.

OCR can significantly reduce the time and effort required to process images and documents. It allows for efficient extraction of text from various types of documents, including those with mixed languages and different handwriting styles. By utilizing Azure Cognitive Services for OCR, organizations can streamline their document processing workflows and ensure accurate Data Extraction.

Form Recognizer for Document Processing

Form Recognizer is another powerful Azure AI service that can help automate document processing. It can accurately extract text, key-value pairs, and tables from documents, allowing you to quickly turn forms into usable data. Form Recognizer is built on top of the Computer Vision Read API and offers additional capabilities to understand the structure of documents, fields, and values.

Layout API in Form Recognizer

The Layout API in Form Recognizer can extract text, tables, and checkboxes from documents. It can detect each cell in a table, as well as numerical values, currency symbols, and characters. The output of the Layout API is a JSON file that provides indexed rows and columns along with their bounding boxes and elements. This allows for a structured representation of the document, making it easier to extract Relevant information.

Pre-built Models in Form Recognizer

Form Recognizer supports pre-built models for specific document types such as sales receipts and business cards. These pre-built models are optimized to extract key information from documents of those types. For example, the business card model can extract information like name, title, address, and company, while the receipt model is designed to extract transaction details from English sales receipts commonly used by businesses like restaurants, gas stations, and retail stores.

Custom Models in Form Recognizer

In addition to pre-built models, Form Recognizer allows you to train custom models with your own data. This is particularly useful when dealing with forms that do not follow standard table layouts. Custom models allow you to tailor the recognition to the specific structure and fields of your forms. Training custom models does not require manual data labeling and can be started with as few as five sample input forms. For better accuracy, it is recommended to use a larger dataset, especially if the form images are of lower quality.

Training custom models in Form Recognizer is facilitated by the Form Recognizer labeling tool. This tool allows you to select fields from your forms and train the model in a Supervised approach. The algorithm used in Form Recognizer employs unsupervised learning to understand the layout and relationships between fields and entries in your forms. When you submit input forms, the algorithm clusters the forms by Type, discovers the keys and tables present, and associates values to keys and entries to tables. This results in the extraction of valuable information from the forms.

By combining OCR capabilities with Form Recognizer, organizations can automate the extraction of structured data from various types of forms. Whether it's detecting tables, checkboxes, or capturing key-value pairs, Form Recognizer provides the necessary tools to streamline document processing workflows.

Enriching Data with Azure Cognitive Search

After extracting text and structured data from documents, the next step is to enrich the data to provide a better understanding of its content. Azure Cognitive Search is a cloud service that offers built-in AI capabilities to enrich all types of information. This enrichment allows for the easy identification and exploration of relevant Content At Scale.

Ingesting Data

The first step in using Azure Cognitive Search is to ingest your unstructured data. This can be done by creating an Azure Cognitive Search resource in the Azure Portal and connecting it to a data source. Alternatively, you can programmatically ingest data from Azure or third-party sources in various formats such as images, audio files, PDFs, CSV, and more.

Enriching Data

Data enrichment involves enhancing the understanding of your data by applying cognitive skills. Azure Cognitive Search provides pre-built AI models and allows you to build custom models to enrich your data. Cognitive skills can include tasks such as image classification, language detection, key phrase extraction, and entity detection. By enriching your data, you can Create new fields in your search index that are not available natively in the source data.

Customizing Search Index

To ensure accurate and efficient search capabilities, the search index needs to be customized. This involves defining index properties such as making fields retrievable, filterable, sortable, and searchable. The Cognitive Search resource automatically identifies metadata related to the files you ingest, but you have the flexibility to customize these properties Based on your specific requirements.

Exploring Data with Azure Cognitive Search

Once the data has been ingested and enriched, it is time to explore the data. Azure Cognitive Search provides several ways to query and analyze the data to gain insights. You can integrate custom models or classifiers built with the language or framework of your choice. You can explore insights through pre-configured search or use analytics tools and business applications to uncover Hidden Patterns and trends in your data.

Azure Cognitive Search supports various file formats and built-in indexers. It also offers support for over 50 languages, making it a versatile tool for exploring data in different contexts. Whether it's querying through APIs or using the Cognitive Search portal, Azure Cognitive Search allows you to transform raw, unstructured information into searchable content, effectively improving your document processing workflow.

Extending Azure AI Solutions

The capabilities provided by Azure AI services go beyond document processing. By integrating with other Azure AI services, organizations can further extend their AI solutions. For example, Personalizer can be used to provide personalized content or document recommendations, while Metrics Advisor can help detect abnormal behavior in a company's data. The integration of OCR, Form Recognizer, and Cognitive Search can be applied to solve a wide range of business challenges, making Azure AI a comprehensive solution for modernizing businesses.

In conclusion, the addition of optical character recognition and AI-powered search capabilities can greatly enhance document processing workflows. With Azure AI services such as OCR, Form Recognizer, and Cognitive Search, organizations can automate the extraction of text, structured data, and insights from various types of documents. The integration of these services streamlines processes, eliminates human errors, and improves the overall efficiency of document processing. By leveraging the power of Azure AI, organizations can modernize their business and stay ahead in today's competitive landscape.

Highlights

• Optical Character Recognition (OCR) allows for the extraction of text from images and documents, reducing manual processing time and errors.
• Form Recognizer enables accurate extraction of text, key-value pairs, and tables from documents, facilitating the conversion of forms into usable data.
• Azure Cognitive Search enriches unstructured data with built-in AI capabilities, providing better understanding and exploration of relevant content at Scale.
• Combining OCR, Form Recognizer, and Cognitive Search automates document processing and enhances efficiency.
• Azure AI services can be extended and integrated for personalized content, anomaly detection, and solving various business challenges.

FAQ

Q: Can OCR extract handwritten text?

A: Yes, Azure Cognitive Services' OCR capabilities can extract both printed and handwritten text from images and documents.

Q: Is Form Recognizer only for standard table layouts?

A: No, Form Recognizer can be trained with custom models to handle forms with different layouts, including key-value structures.

Q: Can Azure Cognitive Search work with different file formats?

A: Yes, Azure Cognitive Search supports various file formats such as images, audio files, PDFs, and CSV, among others.

Q: Is it possible to integrate custom models or classifiers with Azure Cognitive Search?

A: Yes, Azure Cognitive Search allows the integration of custom models or classifiers built using the language or framework of your choice.

Q: Can Azure AI services be applied to business challenges beyond document processing?

A: Yes, OCR, Form Recognizer, and Cognitive Search can be used to solve a wide range of business challenges, including personalized content and anomaly detection.