Image processing is basically the use of computer algorithms to perform image processing on digital images. Digital image processing is a part of digital signal processing. Digital image processing has many significant advantages over analog image processing. Image processing allows a much wider range of algorithms to be applied to the input data and can avoid problems such as the build-up of noise and signal distortion during processing of images. Wavelet transforms have become a very powerful tool for de-noising an image.

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The vegetational landscape of north-easternTerairegion at the foot hills of Central Himalayas is a mosaic of grassland, old-field, wasteland, and forest ecosystems. Like many other parts of the country, this region is also infested with alien intruders which not only interfere with the growth and production of food crops but also exercise adverse effects on the biodiversity of native species. The present study attempts to catalogue the invasive alien species of the terrestrial vegetation of north-eastern Uttar Pradesh especially with reference to their habit, taxonomic position, and nativity. A total of 1135 plant species within 580 genera under 119 families are so far known to occur in the region. Of these, only 149 species within 100 genera under 41 families have been found to be invasive aliens as evident from their center of origin, past history, nature of aggregation, and invasion observed under field conditions. About 80% of these invaders have been introduced from neotropics. Out of 173 invasive plants across India, this region shares 149 species, out of which 66% of species have come from Tropical America, 14% from African continent, and the rest from other countries. A better planning in the form of early identification and reporting of infestation and spread of noxious weeds is needed for their control.

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Abstract:Handwritten character recognition is currently getting the attention of researchers because of possible applications in assisting technology for blind and visually impaired users, human-robot interaction, automatic data entry for business documents, etc. In this work, we propose a technique to recognize handwritten Devanagari characters using deep convolutional neural networks (DCNN) which are one of the recent techniques adopted from the deep learning community. We experimented the ISIDCHAR database provided by (Information Sharing Index) ISI, Kolkata and V2DMDCHAR database with six different architectures of DCNN to evaluate the performance and also investigate the use of six recently developed adaptive gradient methods. A layer-wise technique of DCNN has been employed that helped to achieve the highest recognition accuracy and also get a faster convergence rate. The results of layer-wise-trained DCNN are favorable in comparison with those achieved by a shallow technique of handcrafted features and standard DCNN.

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