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Numerous research based on offline Tamil recognition deals only with few Tamil characters since it becomes extremely complicated in distinguishing small variations in large handwritten document. The writer's complexity affects the overall formation of the characters. Such types of complexities are due to discontinuation of structures, unnecessary over loops, variation in shapes as well as irregular curves. This complex issue results in enhanced error value rate. Therefore, to conquer such issues, this paper proposes a novel approach to enhance the offline Tamil handwritten character recognition by utilizing four principal steps: pre-processing, segmentation, feature extraction and classification. For optimal segmentation of Tamil characters, this paper utilizes the Tsallis entropy approach-based atom search (TEAS) optimization algorithm. Then a Newton algorithm based deep convolution extreme learning (DELM) approach is utilized for the extraction and classification of input images. Finally, experiments are carried out for numerous Tamil handwritten recognition-based approaches. The proposed Tamil character recognition utilizes the datasets of isolated Tamil handwritten characters established by HP lab India to evaluate the efficiency of the system. [ABSTRACT FROM AUTHOR]

India is rich in its heritage and culture. It has many historical monuments and temples where the walls are made of inscribed stones and rocks. The stone inscriptions play a vital role in portraying about the ancient incidents. Hence, the digitization of these stone inscriptions is necessary and contributes much for the epigraphers. Recently, the digitizing of these inscriptions began with the binarization process of stone inscriptions. This process mainly depends on the thresholding technique. In this paper, the binarization of terrestrial and underwater stone inscription images is preceded by a contrast enhancement and succeeded by edge-based filtering that minimizes noise and fine points the edges. A new method called modified bi-level thresholding (MBET) algorithm is proposed and compared with various existing thresholding algorithms namely Otsu method, Niblack method, Sauvola method, Bernsen method and Fuzzy C means method. The obtained results are evaluated with the performance metrics such as peak signal-to-noise ratio (PSNR) and standard deviation (SD). It is observed that the proposed method has an improvement of 49% and 39%, respectively, on an average by the metrics considered. [ABSTRACT FROM AUTHOR]