This paper presents a character recognition system that handles degraded manuscript documents like the ones discovered at the St. Catherineȁ9;s Monastery. In contrast to state-of-the-art OCR systems, no early decision (image binarization) needs to be performed. Thus, an object recognition methodology is adapted for the recognition of ancient manuscripts. The proposed system is based on local descriptors which are clustered in order to localize characters. Finally, a class probability histogram is assigned to each character present in an image which allows for the character classification. The system achieves an F0.5 score of 0.77 on real world data that contains 13.5% highly degraded characters.
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