Integrated segmentation and recognition of connected Ottoman script

In this thesis, a novel context-sensitive segmentation and recognition method for connected letters in Ottoman script is proposed. This method first extracts a set of possible segments from a connected script and determines the candidate letters to which extracted segments are most similar. Next, a function is defined for scoring each different syntactically correct sequence of these candidate letters. To find the candidate letter sequence that maximizes the score function, a directed acyclic graph is constructed. The letters are finally recognized by computing the longest path in this graph. Experiments using a collection of printed Ottoman documents reveal that the proposed method provides very high precision and recall figures in terms of character recognition. In a further set of experiments we also demonstrate that the framework can be used as a building block for an information retrieval system for digital Ottoman archives.