Multi-script handwritten character recognition: Using feature descriptors and machine learning

Handwritten character recognition plays an important role in transforming raw visual image data obtained from handwritten documents using for example
scanners to a format which is understandable by a computer. It is an important application in the field of pattern recognition, machine learning and artificial
intelligence. There are already different handwritten character recognition systems that have been designed for commercial purposes, such as mail sorting and bank cheque processing. Furthermore, this type of research can help to search through different historical handwritten manuscript collections. In this way the cumulative historical information can become accessible to a wide public.

In this PhD research, several methods are proposed to deal with several challenges that occur when trying to recognize handwritten characters from multiple language scripts.
The thesis contributes to all levels of processing isolated character images: from intensity normalization to segmentation, and from feature extraction to the final classification.
Moreover, solutions are proposed for recognizing isolated handwritten character images when not very many handwritten character examples are available.

The main goal of the research presented in this dissertation is to study robust feature extraction techniques and machine learning techniques for handwritten character recognition. The best techniques are the combination of the histogram of oriented gradients with bags of visual words. Furthermore, a new method for line segmentation is proposed, which is a part of document layout analysis. The novel techniques have been tested on many different scripts and the results show that they effectively address the problems of line segmentation and character recognition.