Unsupervised Page Area Detection Approach for the Unconstrained Chronic Handwritten Modi Document Images

The sparse old age historical Modi documents are archived in government and non-government sectors from long time. These documents are pasted on card-sheet like material for preservation. In the processes of intelligent archaic Modi document recognition system there is a need to detect the actual page area before to other preprocessing stages. Over the past period there has been a growing interest in addressing of object detection of document images or removal of clutter noise. However, no comprehensive studies have been exists which are work on the proposed problem statement. This paper presents page boundary extraction and page area detection approach for highly degraded chronic handwritten Modi document images. This algorithm is based on the analysis of the gray level frequency of the document image with statistical scrutiny. The ground-truth region-based evaluation methods are used to evaluate and compare the results. Actual page area of the ancient handwritten Modi document images is detected 97% proficiently without affecting its originality. The proposed approach is robust and efficient compared with state-of-art techniques.