Here you can find additional packages for document analysis problems that are too specific to be included in the Gamera core distribution. These packages are provided as Gamera toolkits, which require a working Gamera installation. They can be installed like Gamera itself with the command

python build && sudo python install

Note that currently only the toolkits marked with an asterisk (*) have been ported to Python 3 and Gamera 4. The other toolkits are currently only available for the old Gamera 3 and Python 2.7.

Text document recognition

The OCR Toolkit* is meant to help building optical character recognition (OCR) systems for standard text documents. It provides:

The GreekOCR Toolkit is an addon for the OCR Toolkit for polytonal (classical) Greek.

Staff line removal

The MusicStaves Toolkit provides algorithms and evaluation methods for staff line detection and removal, an important preprocessing step in Optical Music Recognition (OMR). The toolkit offers the following functionality:

Lute tablature recognition

The OTR Toolkit is a complete application for the recognition of historic lute tablature prints. It offers the following functionality:

Psaltic neume notation recognition

The Psaltiki Toolkit is a complete application for the recognition of the post 1800 neume based chant notation of the eastern church. It offers the following functionality:

Webcam access

The Webcam Toolkit allows for automatically taking a snapshot photo from a webcam or document camera and converting it to the Gamera image data type. It provides

Fourier descriptors

The FD Toolkit implements a wide variety of Fourier descriptors. These are useful features for shape recognition. In contrast to ordinary Fourier descriptors, the FD toolkit also provides desciptors that work on broken shapes.

MIS image file support

The MIS Support Toolkit adds reading support for the "Multiple Image Set" (MIS) image format that is used by the NIST Special Database 19 of the US National Institute of Standards and Technology, a widley used reference data set for OCR evaluation.