No abstract

Reduced area and high speed 2-D DWT structural design is presented here. To decrease the delay in critical path with one multiplier, minimum stages of pipeline stage required are four for a lifting step. To reduce the pipeline stages, here short modification is adopted by recombining and storing the intermediate stages of result value. By adopting this work, wecan reduce register number without critical path extension and scanning architecture adopted is parallel two inputs/ two outputs architecture, and it increases the speed and critical path can be decreased to a delay of a multiplier by 3 pipeline stages.By adopting shift add method for multiplication, the proposed method able to decrease the delay in critical path to delay of a adder. The number of registers between column and row filter requires is three for this proposed architecture for NXN size 2-DWT.Index Terms-Discrete wavelet transform (DWT), lifting scheme, pipeline, VLSI architecture.

A novel method to detect the text region from the natural image using the discriminative deep feature of text regions is presented with deep learning concept in this manuscript. Curve text detection (CTD) from the natural image is generally based on two different tasks: learning of text data and text region detection. In the learning of text data, the goal is to train the system with a sample of letters and natural images, while, in text region detection, the aim is to confirm the detected regions are text region or not. The emphasis of this research is on the development of deep learning algorithm. A novel approach has been proposed to detect the text region from natural images which simultaneously tackles three combined challenges: 1) pre-processing of the image without losing text region; 2) appropriate segmentation of text region using their strokes, and 3) training of data. In pre-processing, image enhancement and binarization are done then morphological operations are defined with the Maximally stable extremal region (MSER) based segmentation technique which operates on the basis of stroke region of text and then finds out the (Speed Up Robust Feature) SURF key point from those regions. Based on the SURF feature, text region is detected from the images using a trained structure of Artificial neural network (ANN) which is based on deep learning mechanism. CTW-1500 dataset is used to simulate the proposed work and the parameters like Precision, Recall, F-Measure (H-mean), Execution time, Accuracy and Error Rate are computed and are compared with the existing work to depict the effectiveness of the work.