Java API for Optical Mark Recognition and Processing in Images
Do you need a simple yet feature-rich Java OMR library? Do you want to recognize optical marks in scanned images? Try Aspose.OMR for Java - A Java class library to perform the Optical Mark Recognition (OMR) operations in Java-based applications. Let’s have a quick walk through the features of the said Java API to see how to recognize optical marks in a variety of image formats and capture human-marked data from surveys, questionnaires or tests containing MCQs.
Configure Threshold for Optical Markers using Java OMR API
We are pleased to announce the release of Aspose.OMR for Java 1.9.0. This month’s release contains all the features, enhancements and bug fixes from Aspose.OMR for .NET 1.8.0 and 1.9.0.
This outlines the most significant changes. Check the detailed release notes for all enhancements and fixes when downloading the latest version of Aspose.OMR for Java 1.9.0.
Optical Mark Recognition Threshold The threshold specifies the percentage of black pixels over which the option is considered to be selected.
Configure Threshold for OMR with Aspose.OCR for .NET
 from the download section along with the required resource files.
Here is a look at a few of the features in this month’s release. For a full list of bug fixes and improvements please refer to the download pages in the link above.
Optical Mark Recognition Threshold The threshold specifies the percentage of black pixels over which the option is considered selected. A threshold can be set before initializing the OMR procedure to determine the amount of pixels (as a percent) that is required before a field or a mark is considered checked or filled.
Support for Optical Mark Recognition (OMR) with Aspose.OCR for Java 1.7.0
We are pleased to announce the release of Aspose.OCR for Java 1.7.0. In this release we have introduced Optical Mark Recognition (OMR). The Aspose.OMR package can be used to recognize different marked elements in a scanned image. Recognition is processed based on a template that contains graphical mapping of the elements to be recognized from the scanned images. Programmers can now load templates and images, and extract data based on these.