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Handwritten optical character recognition (OCR) is a noteworthy research region because of its sensitivity in segmenting the character which increments on account of MARATHI script because of modifiers and compound characters. This paper gives a streamlined OCR framework for handwritten MARATHI text document classification and recognition system. To deal with a vast measure of features, the support vector machine (SVM) assumes a noteworthy part which was likewise used for the classification reason. In this paper, we display a projection profile segmentation technique which generates less error. The Curvelet Transform (CT) to be exceptionally efficient and hearty to get the feature characters from the pre-processed image. The extracted feature sets are decreased by Principle Component Analysis (PCA) algorithm. After the feature extraction process, the Adaptive Cuckoo Search (ACS) algorithm is used for the optimization procedure. Here, the written by hand MARATHI script was segmented flexibly in three levels; (1) line segmentation, (2) word segmentation and (3) character segmentation. The preprocessing was finished utilizing different morphological operations. The experimental results show that, the performance of the proposed technique is assessed in view of the accuracy, sensitivity, precision, recall and F-score. Compared with the existing Fire Fly Selection (FFS) and Bat Selection (BS) approach, the proposed method has 99.36% accuracy, 90% sensitivity, 91% precision, 89.51% recall, 99.67% specificity and 89.93% F-score. The proposed approach is actualized using MATLAB and the realtime Marathi character datasets are used for our examination.

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