Key Conceptsīefore you learn about the use cases and implementation of Optical Character Recognition, it's important to understand its fundamental concepts in detail. Refer to the API documentation to learn about the API available for OCR.
You can refer to the Java SDK documentation and Node.js SDK documentation for code samples of Zia OCR. You can also test Zia OCR by uploading sample images or documents that contain text in the console and obtain the recognized text, to get a better idea of Zia's accuracy and the OCR response format. The Catalyst console provides easy access to code templates for these environments that you can implement in your application's code.
The identified text can be stored digitally or used for further data processing.Ĭatalyst provides Zia OCR in the Java and Node.js SDK packages, and you can integrate it in your Catalyst web or Android application. OCR is widely used in web and mobile applications that are created to read content from scanned or photographed documents, flyers, menus, posters, signs, and other files containing text. Zia OCR can automatically detect and recognize texts in 10 major languages. You can code the Catalyst application to store the recognized data or process it further in any way you require.
The recognized text is presented as a JSON response, along with a confidence score that informs you of its accuracy. The recognized text is then presented as a JSON response. Zia detects text in photos and scanned documents, then breaks the text down into individual characters, and identifies the language it is in. Optical Character Recognition is a Catalyst Zia AI-driven service that performs the electronic detection of handwritten or printed textual characters in images or digital documents, and converts the detected characters to machine-encoded text. Access Code Templates for Optical Character Recognition.Test Optical Character Recognition in the Catalyst Console.