Decode with Einstein: Optical Character Recognition Explained
Table of Contents:
- Introduction to Optical Character Recognition
- About the Einstein Platform Services
- Overview of Optical Character Recognition (OCR)
- Reviewing the Einstein OCR Pilot Program
- Step-by-Step Development Guide for Setting Up OCR in Salesforce
- Demos and Art of the Possible
- Common Use Cases for Einstein OCR
- Future of Einstein OCR and Potential Applications
- Recommendations and Call to Action
Introduction to Optical Character Recognition
Optical Character Recognition (OCR) is a revolutionary technology that allows for the conversion of images of Typed, handwritten, or printed text into machine-encoded text. This technology has been around for over a century, with its original use case being the creation of reading devices for the blind. Today, OCR plays a vital role in automating repetitive tasks and saving time from manual data entry.
About the Einstein Platform Services
The Einstein Platform Services encompass a wide range of products and tools within the Einstein platform. While this article focuses on OCR, it's essential to understand that Einstein Platform Services go beyond OCR and include products such as Einstein Analytics and Einstein Language. These services leverage artificial intelligence (AI) to provide advanced capabilities for various use cases, including text translation, sentiment analysis, and more.
Overview of Optical Character Recognition (OCR)
OCR technology scans images of text documents and identifies the characters to convert them into editable and machine-readable text. OCR is commonly used to extract data from standardized documents, such as contracts, invoices, and business cards. By automating the process of data entry, OCR significantly reduces the time and effort required to capture information accurately.
Reviewing the Einstein OCR Pilot Program
Einstein OCR is currently in a pilot program, meaning it's not generally available yet. However, interested users can Apply for the pilot program through their Salesforce Account Executive (AE). Upon acceptance into the program, users can download the necessary unmanaged Package via the AppExchange. The package includes three APEX classes and one Apex trigger, providing access to the AI Tools and ML services required for OCR implementation.
Step-by-Step Development Guide for Setting Up OCR in Salesforce
In this section, we will provide a detailed guide on how to set up OCR in your Salesforce org. The process involves several steps, starting with downloading the unmanaged package and configuring the OCR service to Read and extract text from uploaded files. We will cover the necessary formulas, triggers, and flows to break down the extracted text and store it in custom fields for further processing and automation.
Demos and Art of the Possible
In this section, we will showcase various demos to illustrate the capabilities of Einstein OCR in real-world scenarios. These demos will include capturing customer details from Dreamforce badges, extracting data from driver's licenses, and parsing information from other standardized documents. Through these demos, readers will gain a practical understanding of how OCR can be applied in different use cases.
Common Use Cases for Einstein OCR
Einstein OCR caters to a wide range of use cases involving the extraction and analysis of textual information. We will explore some of the most common applications, such as capturing data from business cards, reading standardized documents, tracking vehicle identification numbers (VIN), and gathering information from driver's licenses. These examples highlight the versatility of OCR and its potential to automate processes in various industries.
Future of Einstein OCR and Potential Applications
As Einstein OCR evolves, its capabilities are expected to expand further. This section focuses on the future of OCR and potential applications beyond its Current functionalities. We will explore the integration of OCR with named entity recognition (NER), sentiment analysis, language translation, and other Einstein Platform Services. By leveraging these tools together, users can achieve advanced automation and data analysis, opening up new possibilities for OCR implementation.
Recommendations and Call to Action
We provide a list of recommendations and actionable steps for readers interested in implementing OCR in their Salesforce org. This includes identifying suitable use cases, applying for the pilot program, utilizing formulas, triggers, and flows to enhance OCR functionality, and sharing experiences and ideas on social platforms using the hashtag #EinsteinOCR. Additionally, we encourage readers to explore the potential of OCR in conjunction with other Einstein Platform Services, maximizing the value and capabilities of AI in their Salesforce environment.
FAQ
Q: Is OCR supported on mobile devices?
A: Yes, OCR is supported on mobile devices, allowing users to capture and extract text from images through their smartphones or tablets.
Q: Can OCR read handwritten text?
A: Currently, OCR is designed to work with printed text and does not support handwritten text recognition. However, support for handwritten OCR is expected in future releases.
Q: What file formats does OCR support?
A: OCR supports JPEG and PNG image formats for text extraction. Other file formats, such as PDFs, are not supported at the moment but are on the roadmap for future updates.
Q: How does OCR determine the order of detected text?
A: OCR analyzes factors like the size of the text and its placement on the image to determine the order of detected text. However, future releases may offer more flexibility and customization options for the order of returned text.
Q: Can OCR extract data from tables?
A: Yes, OCR can extract data from tables if the image contains a table with text. This feature is valuable for processing invoices, forms, or other structured documents.
Q: Are there any limitations on font types with OCR?
A: OCR does not have specific limitations on font types. It can read a wide range of fonts as long as the text is printed and within the supported languages.
Q: Can OCR extract specific information Based on keywords?
A: Currently, OCR does not offer direct support for extracting specific information based on keywords (key-value pairs). However, this feature is on the roadmap and may be available in future releases.
Q: How can OCR be used with named entity recognition (NER)?
A: By combining OCR with NER, users can extract and classify named entities, such as people or locations, from the recognized text. This integration enhances the accuracy and usefulness of extracted information.
Q: Can OCR process text in multiple languages?
A: OCR currently supports languages that use the Latin script. However, languages like Japanese or Hebrew are not supported. Language translation capabilities are being developed to address this limitation in future updates.
Q: Can OCR be used to scan and process knowledge articles?
A: While OCR is primarily designed for processing text from images, it can potentially be utilized in scanning and processing knowledge articles. This application can streamline the content creation process and improve knowledge management within organizations.
Q: Can OCR handle different formats of Dreamforce badges?
A: Yes, OCR can process different formats of Dreamforce badges. By analyzing the text and making necessary adjustments to formulas and triggers, OCR can effectively extract the desired information, such as attendees' names and companies, from various badge formats.
