We are pleased to announce the release of Aspose.OCR for Java 2.5.0 . In this version, you will find Improved OCR engine performance, improved Barcode element recognition process and how to create OMR templates dynamically & efficiently. If you are planning to upgrade the API from previous revision, go ahead as there is no change in the API. In addition to OMR Template Editor application used to create the templates, developers can also dynamically create templates using the code. This release has addressed a few critical issues and has incorporated some enhancements. The details are enhanced the OCR Engine performance, improved the BarCode element recognition process and dynamically create OMR templates without any issue. As always, we appreciate your feedback so if you ever have anything to tell us about this release or anything else, please head to the Aspose.OCR forum for a chat. This release includes plenty of new & enhanced features as listed below
- Improve recognition performance on common usage scenarios
- Remove Examples Dashboard from Release Packages
- java.lang.NullPointerException during Data Extraction for BarCode element
- How to dynamically create OMR templates?
- OCR for Java 2.4 should support template 4 version
- Merge OMR functionality into Aspose.OCR assembly
- Incorrect OCR results for a 300dpi image with numbers on it
- Support Brazilian Portuguese
- Incorrect recognized results from a 300 DPI sample
Newly added documentation pages and articles
Some new tips and articles have now been added into Aspose.OCR for Java documentation that may guide you briefly how to use Aspose.OCR for performing different tasks like the followings.
Aspose.OCR for Java is a character recognition component that allows developers to add OCR functionality in their Java web applications, web services and Windows applications. It provides a simple set of classes for controlling character recognition tasks. It helps developers to work with image files from within their Java applications. It allows developers to extract text from images, Read font, style information quickly, saving time & effort involved in developing an OCR solution from scratch.