This paper addresses the problems encountered during digitization and preservation of inscriptions such as perspective distortion and minimal distinction between foreground and background. In general inscriptions possess neither standard size and shape nor colour difference between the foreground and background. Hence the existing methods like variance based extraction and Fast ICA based analysis fail to extract text from these inscription images. Natural gradient flexible ICA (NGFICA) is a suitable method for separating signals from a mixture of highly correlated signals, as it minimizes the dependency among the signals by considering the slope of the signal at each point. We propose an NGFICA based enhancement of inscription images. The proposed method improves word and character recognition accuracies of the OCR system by 65.3% (from 10.1% to 75.4%) and 54.3% (from 32.4% to 86.7%), respectively.
Coronavirus is a contagious disease that affects individuals in a large scale. Coronavirus had a huge impact on the nation's economy and human lifestyle. The motivation behind this study was establishing a better diagnosis test for coronavirus infection. The RT-PCR test is used to diagnose the coronavirus frequently and returned a negative result for an infected individual. Furthermore, this test remains prohibitively expensive for most citizens, and not everyone could afford it due to financial hardship. An efficient imaging approach is de veloped for the evaluation of lung conditions, which has been done by examining the chest X-ray or chest CT of an infected person. Deep Learning is the well-suited sub domain of Artificial Intelligence [AI] technology, which offers helpful examination to consider more number of chest X-rays images that can basically have an effect on coronavirus screening. The goal of this research is to cluster the radiograph images present in the dataset into COVID-19, healthy and viral pneumonia by making use of the artificial neural networks. The training dataset was fine-tuned with eleven previously trained convolutional neural architectures. The assessment of the models on a test sample shows that AlexNet, DenseNet-121, GoogleNet and S queezenet1.1 as the top performing models.
With the advent of technology, we are getting more comfortable with the use of gadgets, cameras, etc., and find Artificial Intelligence as an integral part of most of the tasks we perform throughout the day. In such a scenario, the use of cameras and vision-based sensors comes as an escape from many real-time problems and challenges. One major application of these vision-based systems is Indoor Human Activity Recognition (HAR) which serves in a variety of scenarios ranging from smart homes, elderly care, assisted living, and human behavior pattern analysis for identifying any abnormal behavior to abnormal activity recognition like falling, slipping, domestic violence, etc. The effect of HAR in real time has made the area of indoor activity recognition a more explored zone by the industrial segment to attract users with their products in multiple domains. Hence, considering these aspects of HAR, this work proposes a detailed survey on indoor HAR. Through this work, we have highlighted the recent methodologies and their performance in the field of indoor activity recognition. We have also discussed- the challenges, detailed study of approaches with real-world applications of indoor-HAR, datasets available for indoor activity, and their technical details in this work. We have proposed a taxonomy for indoor HAR and highlighted the state-of-the-art and future prospects by mentioning the research gaps and the shortcomings of recent surveys with respect to our work.
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