Autonomously normalized horizontal differentials as features for HMM-based Omni font-written OCR systems for cursively scripted languages

Automatic font-written Optical Character Recognition (OCR) is highly desirable for numerous modern information technology (IT) applications. Reliable font-written OCR´s for Latin scripts are readily in use since long. For cursively scripted languages, that are the mother tongues of over one fourth of the world population, such OCR´s are however not available at a robust and reliable performance. In this regard, the main challenge is the mandatory connectivity of characters/ligatures (i.e. graphemes) that has to be resolved simultaneously upon the recognition of these graphemes. Among the various approaches tried over decades, Hidden Markov Models (HMM)-based OCR´s seem to be the most promising as they capitalize on the ability of HMM decoders to achieve segmentation and recognition simultaneously similar to the widely used HMM-based automatic speech recognition (ASR). Unlike ASR´s, what is missing in HMM-based OCR´s is the definition of a rigorously founded features vector capable to robustly achieving minimal “font type/size-independent” (omnifont) word error rates comparable to those realized with Latin scripts. Here comes the contribution of this paper that introduces such a sound features vector design, and experimentally shows its superiority in this regard.