Abstract-Recognition of Indian language scripts is a challenging problem. Work for the development of complete OCR systems for Indian language scripts is still in infancy. Complete OCR systems have recently been developed for Devanagri and Bangla scripts. Research in the field of recognition of Telugu script faces major problems mainly related to the touching and overlapping of characters. Segmentation of touchingTelugu characters is a difficult task for recognizing individual characters. In this paper, the proposed algorithm is for the segmentation of touching Hand written Telugu characters. The proposed method using Drop-fall algorithm is based on the moving of a marble on either side of the touching characters for selection of the point from where the cutting of the fused components should take place. This method improvers the segmentation accuracy higher than the existing one.
Over the past several years there has been considerable attention focused on compression and enhancement of speech signals. This interest is progressed towards the development of new techniques capable of producing good quality speech at the output. Speech compression is a process of converting human speech into effi cient encoded representations that can be decoded to produce a close approximation of the original signal. In this paper the simulated vocoder (LPC) using mat lab was implemented for compression. The result obtained from LPC was compared with other implemented speech compression using Wavelet transform in terms of quality. Speech enhancement means improving the quality or value of the signal in a noisy environment. In this paper we proposed spectral subtraction (S.S) and wavelet transform methods for denoising and the result of one method was compared with other. From the results we see that in both compression and enhancement, the performance of wavelet transform was better than other.
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