A large volume of legacy documents in Indian languages exist only in paper form. Web based interactive access techniques for images of these documents can ensure wider dissemination and easy availability. In this paper, we have proposed an access mechanism based on word based indexing and personalized annotation. The word based indexing scheme exploits typical structural characteristics of Indian scripts. We have combined this word indexing technique with personalized annotation based hyperlinking and query scheme for providing an interactive access interface to a collection of Indian language documents.

Continuous observation and management of agriculture are essential to estimate crop yield and crop failure. Remote sensing is cost-effective, as well as being an efficient solution to monitor agriculture on a larger scale. With high-resolution satellite datasets, the monitoring and mapping of agricultural land are easier and more effective. Nowadays, the applicability of deep learning is continuously increasing in numerous scientific domains due to the availability of high-end computing facilities. In this study, deep learning (U-Net) has been implemented in the mapping of different agricultural land use types over a part of Punjab, India, using the Sentinel-2 data. As a comparative analysis, a well-known machine learning random forest (RF) has been tested. To assess the agricultural land, the major winter season crop types, i.e., wheat, berseem, mustard, and other vegetation have been considered. In the experimental outcomes, the U-Net deep learning and RF classifiers achieved 97.8% (kappa value: 0.9691) and 96.2% (Kappa value: 0.9469), respectively. Since little information exists on the vegetation cultivated by smallholders in the region, this study is particularly helpful in the assessment of the mustard (Brassica nigra), and berseem (Trifolium alexandrinum) acreage in the region. Deep learning on remote sensing data allows the object-level detection of the earth’s surface imagery.

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Agriculture land is playing a vital role in developing the economy of Indian states and contributes ~ 15% of India's gross domestic product (GDP). Moreover, agriculture is a major source of livelihood by engaging two-third (~ 66%) of the nation's population in various activities such as food supply, the raw material to the industries, internal and external trade. Therefore, the continuous monitoring and mapping of agricultural land are crucial for the sustainable life and development of the country. Most of the agriculture monitoring solutions are based on field observations or conventional strategies which are time-consuming and costlier. However, remote sensing delivers a cost-effective solution of acquiring information regarding the healthy or unhealthy vegetation in agricultural land with the help of a diverse range of advanced geospatial techniques such as classification, change detection, and pan-sharpening. In the present paper, we have performed a systematic survey with respect to recent advancements made in the classification algorithm, especially for agricultural land. These emerging methods incorporated in classifiers are machine learning and deep learning to enhance and detect the various features of vegetation parameters. It is expected that such studies will provide effective guidance to the researchers in better understanding the features, limitations, and specific importance of emerging classifiers in the Agriculture domain.

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