An OpenStack Cloud Solution for a Community Database with Handwritten Characters Used in Developing OCR Algorithms

Most research addressing OCR through machine learning techniques is focused on the actual algorithms and on using the MNIST data set as the de facto benchmark. Little effort was made to extend the data set or to build an entirely new one. Furthermore, support for characters other than English ones is mostly limited. This paper presents an OpenStack based approach that aims to overcome this last limitation by providing a community-oriented solution for developing and maintaining richer, language agnostic, community-shared data sets for OCR based applications. The proposed architecture is integrated with OpenStack services and relies on new Cloud perspectives, such as Function-as-a-Service (FaaS), to achieve a greater degree of flexibility. The included modules allow users to upload their own data sets, select or fine-tune their desired pre-processing methods, and derive the required features for their target character set. Both the input and the output data are stored using OpenStack specific data services and are shared for all the users of the Cloud deployment. An interesting feature is that the underlying FaaS functionality would also allow interested parties to upload their own pre-processing and feature extraction stages.