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Open source ocr tool for .net1/4/2023 OPEN SOURCE OCR TOOL FOR .NET LICENSEAutomatic license plate readers are the most widely used tool for vehicle identification. Here, two areas can be mainly distinguished: person and vehicle identification. Security cameras have been proven to be particularly useful in preventing and combating crime through identification tasks. The developed method can be easily transferred to other material systems and nanoparticle structures. Finally, the application of the algorithm to bimetallic nanoparticles demonstrates the automated data collection of size distributions including classification of complex ultrastructures. We show how the generation of synthetic images, either using image processing or using various image generation neural networks, can be used to improve the results in both stages. For each stage, we optimize the segmentation and classification by analysis of the different state-of-the-art neural networks. Our approach is comprised of two stages: localization, i.e., detection of nanoparticles, and classification, i.e., categorization of their ultrastructure. In this paper, we present a deep-learning based method for nanoparticle measurement and classification trained from a small data set of scanning transmission electron microscopy images. The results show that the proposed system gives good recognition rates and an accuracy of up to 99.9% for training done on the IAM dataset and an accuracy ofĨ8.6% for tests done on all of the medical prescription data.Īccurately measuring the size, morphology, and structure of nanoparticles is very important, because they are strongly dependent on their properties for many applications. For this, we will use two datasets (IAM and medical prescriptions) whose objective is to segment them and classify them in predefined characters. The system to be established shows how artificial neural networks (ANNs) are used to develop a model capable of recognizing the handwriting of a medical practitioner using the deep recurrent convolutional neural network CRNN. This dissertation covers pattern recognition with a particular focus on the ocerization of handwritten texts.We are going to propose a system that works on handwritten recognition and we will try it more precisely on medical prescriptions. Read is that doctors use abbreviations and medical terminology that most pharmacists don't understand. Part of the reason doctors' prescriptions are hard to Reading a doctor's handwritten prescription is a challenge that most patients and some pharmacists face a problem which in some cases leads to negative consequences due to incorrect decryption of the prescription. The quality and efficiency of the proposed solution indicate that it is suitable for practical implementation in onshore monitoring systems. Obtained identification times have not exceeded 1s. Our method recognised 91% of vessels from our test dataset. The proposed method works with low-quality images with inscriptions placed under different angles and different, readable sizes. OPEN SOURCE OCR TOOL FOR .NET REGISTRATIONThe main contribution of this research is a method that can identify any type of vessel in an image that has visible inscriptions (name, registration number) placed on the hull and must be registered in a public registry. The automation of the process has met many challenges related to the often-low quality of available video streams, heterogeneous regulations on the marking of ships, and the specifics of natural scene text recognition, such as quickly alternating imaging conditions or the interference of the background. The obtained information can be then matched with available ship registers. Readable vessel plates and hull inscriptions of detected ships in the video stream allow using text location and recognition methods to obtain ships’ identification names or numbers. Existing video monitoring systems help visually identify a vessel where other systems are not present or sufficient. The identification of ships plays a crucial role in security and managing vessel traffic for ports and onshore facilities.
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