Optical Character Recognition (OCR) has been a regarded research topic in the last century. Nowadays the main issues of OCR are solved. Since OCR systems are base upon binary images of the text, their results are still poor if the text is degraded. In this thesis a codex consisting of ancient manuscripts is investigated. Due to environmental effects the characters of the analyzed codex are washed out which leads to poor results gained by state of the art binarization methods. Hence, a segmentation free approach based on local descriptors is being developed. Regarding local information allows for recognizing characters that are only partially visible and have a high interclass variance due to different writers. In order to recognize a character the local descriptors are initially classified with a Support Vector Machine (SVM) and then identified by a voting scheme of neighboring local descriptors. In this talk preliminary results of the proposed method are presented and future developments will be discussed.