Chandrani Singh | Searcharxiv

arXiv2023

Text Finder Application for Android

A Text Finder, an android application that utilizes Optical Character Recognition (OCR) technology with the help of Google Cloud Vision API to extract text from images taken with the device camera or from existing images in the users phone. The extracted text can be saved to the device storage where all previous extracts can be easily accessed on a user-friendly interface. The application also features editing, deletion and sharing options for the extracted text. The user interface is user-friendly, making the application accessible to students, professional and organizations for a variety of purposes, including document scanning, data entry, and information retrieval. Manual extraction of text by typing or writing from images can be very time-consuming and can be prone to errors. This application is an efficient and simple solution for extracted texts and organizing important information from the photos. This paper describes the technical details of the OCR technology and Googles ML Kit Text Recognition API used in the application, as well as the design, implementation and evaluation of the application in terms of performance and accuracy. The research also explores the key objectives and benefits of Text Finder, such as reducing the time and effort required and increasing the efficiency of document-based tasks.

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arXiv2021

A Novel Approach for Earthquake Early Warning System Design using Deep Learning Techniques

Earthquake signals are non-stationary in nature and thus in real-time, it is difficult to identify and classify events based on classical approaches like peak ground displacement, peak ground velocity. Even the popular algorithm of STA/LTA requires extensive research to determine basic thresholding parameters so as to trigger an alarm. Also, many times due to human error or other unavoidable natural factors such as thunder strikes or landslides, the algorithm may end up raising a false alarm. This work focuses on detecting earthquakes by converting seismograph recorded data into corresponding audio signals for better perception and then uses popular Speech Recognition techniques of Filter bank coefficients and Mel Frequency Cepstral Coefficients (MFCC) to extract the features. These features were then used to train a Convolutional Neural Network(CNN) and a Long Short Term Memory(LSTM) network. The proposed method can overcome the above-mentioned problems and help in detecting earthquakes automatically from the waveforms without much human intervention. For the 1000Hz audio data set the CNN model showed a testing accuracy of 91.1% for 0.2-second sample window length while the LSTM model showed 93.99% for the same. A total of 610 sounds consisting of 310 earthquake sounds and 300 non-earthquake sounds were used to train the models. While testing, the total time required for generating the alarm was approximately 2 seconds which included individual times for data collection, processing, and prediction taking into consideration the processing and prediction delays. This shows the effectiveness of the proposed method for Earthquake Early Warning (EEW) applications. Since the input of the method is only the waveform, it is suitable for real-time processing, thus the models can also be used as an onsite EEW system requiring a minimum amount of preparation time and workload.

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