CellWriter is a grid-entry natural handwriting input panel. As you write characters into the cells, your writing is instantly recognized at the character level. When you press 'Enter' on the panel, the input you entered is sent to the currently focused application as if typed on the keyboard. Writer-dependent, CellWriter learns your handwriting for reliable recognition. Correcting preprocessor algorithms account for digitizer noise, differing stroke order, direction, and number of strokes. Unicode support enables you to write in any language.
Inlab-Scheme is an independent implementation of the algorithmic language Scheme and conforms to the R4RS and the IEEE Standard 1178. In addition to the language core, Inlab-Scheme has support for image processing which allows the implementation of OCR and image recognition applications. Inlab-Scheme comes with two built in graphic file format converters which convert the PATIMG patent file format and the ST.33 patent file format to multipage TIFF without decompressing.
The LTI-Lib is an object oriented library with algorithms and data structures frequently used in image processing and computer vision. It was developed at the RWTH-Aachen University as a part of many research projects on computer vision dealing with robotics, object recognition, sign language, and gesture recognition. It provides an object oriented C++ library that includes fast algorithms, which can be used in real applications.
Mimas Toolkit is a C++ computer vision toolkit. It is easy to use and includes tools for edge detection, corner detection, various filters, optic flow, tracking, blob analysis, Web cam tools for real-time applications, and much more. It also includes many implementations of traditional algorithms such as Canny. It was developed for GNU/Linux but as the GUI is largely separate, porting to other platforms should be straightforward.
The OMCSNet-WordNet project aims to improve the quality of the OMCSNet dataset by using automated processes to map WordNet synonym sets to OMCSNet concepts and import additional semantic linkage data from WordNet. It is based on OMCSNet 1.2, a semantic network and inference toolkit written in Python/Java. OMCSNet currently contains over 280,000 separate pieces of common sense information extracted from the raw OMCS dataset. This project is also based on WordNet, an online lexical reference system that in recent years has become a popular tool for AI researchers.
OMCSNetCPP is a C++ API and inference toolkit for accessing OMCSNet, a semantic network mined out of the Open Mind Common Sense knowledge base. The goal of this project is to provide a class library that allows programmers to easily add common sense reasoning capabilities to C++ applications.
The Pattern Recognition Application Programmer's Interface aims to be a fully-featured, easy-to-use general C++ framework for various pattern recognition tasks, especially image analysis. It features support for many image formats, well-known image analysis methods, classification and feature analysis tools, XML serialization, etc.