  Recognition of handwritten document aims at transforming document images into
a machine understandable format. Handwritten document recognition is the most
challenging area in the field of pattern recognition. It becomes more complex
when a document was written on vellum before hundreds of years, like older Geez
scripts. In this study, we introduced a modified segmentation approach to
recognize older Geez scripts. We used adaptive filtering for noise reduction,
Isodata iterative global thresholding for document image binarization, modified
bounding box projection to segment distinct strokes between Geez characters,
numbers, and punctuation marks. SVM multiclass classifier scored 79.32%
recognition accuracy with the modified segmentation algorithm.
