Modeling of a Method of Cellular Technology Processing Systems and Pattern Recognition Images for Fast Recognition of Dynamic Images

Supervised learning has been considered as a hot topic as it is used in different fields that can exploit the advantages of artificial intelligence. This research introduces a new approach using cellular technology for solving various problems of processing and pattern recognition images that are invariant to the orientation, scale, and dynamic changes in real time. On the basis of the notion of geometric type solved the problem of information selection elements in the image recognition of shapes, lines and laser processing of personal identification for handwritten text. Keywords: cellular technology, pattern recognition, figures recognition, neural networ

A Comprehensive Review of Deep Learning Architectures for Computer Vision Applications

The emergence of machine learning in the artificial intelligence field led the world of technology to make great strides. Today’s advanced systems with the ability of being designed just like human brain functions has given practitioners the ability to train systems so that they could process, analyze, classify, and predict different data classes. Therefore, the machine learning field has become a hot topic for scientists and researchers to introduce the best network with the highest performance for such mentioned purposes. In this article, computer vision science, image classification implementation, and deep neural networks are presented. This article discusses how models have been designed based on the concept of the human brain. The development of a Convolutional Neural Network (CNN) and its various architectures, which have shown great efficiency and evaluation in object detection, face recognition, image classification, and localization, are also introduced. Furthermore, the utilization and application of CNNs, including voice recognition, image processing, video processing, and text recognition, are examined closely. A literature review is conducted to illustrate the significance and the details of Convolutional Neural Networks in various applications

OCR Text Extraction

This research tries to find out a methodology through which any data from the daily-use printed bills and invoices can be extracted. The data from these bills or invoices can be used extensively later on – such as machine learning or statistical analysis. This research focuses on extraction of final bill-amount, itinerary, date and similar data from bills and invoices as they encapsulate an ample amount of information about the users purchases, likes or dislikes etc. Optical Character Recognition (OCR) technology is a system that provides a full alphanumeric recognition of printed or handwritten characters from images. Initially, OpenCV has been used to detect the bill or invoice from the image and filter out the unnecessary noise from the image. Then intermediate image is passed for further processing using Tesseract OCR engine, which is an optical character recognition engine. Tesseract intends to apply Text Segmentation in order to extract written text in various fonts and languages. Our methodology proves to be highly accurate while tested on a variety of input images of bills and invoices

Pengenalan Pola Berbasis OCR untuk Pengambilan Data Bursa Saham

The investor must be able to use instinct to evaluate when to sell and buy stocks. This is, of fact, a weakness for inexperienced investors, in addition to the decision's inaccuracy and the time it takes to evaluate a slew of ineffective results. So that, a support system is needed to help the investors make decisions in buying and selling shares. This support system creates an online analysis curve display through text data in the BEI stock price application. The data processing based on pattern recognition will be carried out so that a buying and selling decision can be made to calculate the profit and loss by investors. As the first step of the whole system, this research has built an image-to-text conversion system based on OCR (Optical Character Recognition) that can convert the non-editable text (.jpg) to be editable (.text) online. After obtaining this .text data, the will used the system in further research to analyze stock buying and selling decisions. According to research on eight companies, the OCR-based image to text conversion has a 96.8% accuracy rate. Meanwhile, using Droid serif, Takao PGhotic, and Waree fonts at 12pt font sizes, it has 100 percent accuracy in Libre Office. Investors in buying and selling shares must analyze when to sell and buy stocks based on instinct. For novice investors, this is a weakness in addition to the inaccuracy of the decision and the time it takes to analyze some ineffective data. This study proposes a solution utilizing OCR (Optical Character Recognition) technology that can convert non-editable text to editable text and allow it to be done online. The application of OCR in this research is to take the text on the IDX stock price data chart so that data processing can be carried out with the principle of recognition patterns so that a buying and selling decision can be calculated by investors predicting stock price fluctuations. The eight companies' testing results for the OCR-based image-to-text conversion obtained 96.8% accuracy. Meanwhile, for testing at the libre office, it has 100% accuracy using the Droid serif, Takao PGhotic, and Waree fonts

Blur2sharp: A gan-based model for document image deblurring

The advances in mobile technology and portable cameras have facilitated enormously the acquisition of text images. However, the blur caused by camera shake or out-of-focus problems may affect the quality of acquired images and their use as input for optical character recognition (OCR) or other types of document processing. This work proposes an end-to-end model for document deblurring using cycle-consistent adversarial networks. The main novelty of this work is to achieve blind document deblurring, i.e., deblurring without knowledge of the blur kernel. Our method, named “Blur2Sharp CycleGAN, ” generates a sharp image from a blurry one and shows how cycle-consistent generative adversarial networks (CycleGAN) can be used in document deblurring. Using only a blurred image as input, we try to generate the sharp image. Thus, no information about the blur kernel is required. In the evaluation part, we use peak signal to noise ratio (PSNR) and structural similarity index (SSIM) to compare the deblurring images. The experiments demonstrate a clear improvement in visual quality with respect to the state-of-the-art using a dataset of text images

Research Study on basic Understanding of Artificial Neural Networks

Artificial neural networks are a computing system inspired by human neuron, designed to simulate the way human brain analyzes and processes information. They are the foundation of artificial intelligence and machine learning technology. This research paper focuses on the basic understanding of Artificial neural networks. ANN create a lots of excitement in Machine learning research and that results a huge development on many AI and machine learning systems like text processing, speech recognition, image processing. Neural networks consist of input and output layers, in many cases hidden layer consisting of units that transform the input into something that the output layer can use. They are essential tools for finding patterns which are far too complex or numerous for a human programmer to extract and teach the machine to recognize

Research Study on basic Understanding of Artificial Neural Networks

Artificial neural networks are a computing system inspired by human neuron, designed to simulate the way human brain analyzes and processes information. They are the foundation of artificial intelligence and machine learning technology. This research paper focuses on the basic understanding of Artificial neural networks. ANN create a lots of excitement in Machine learning research and that results a huge development on many AI and machine learning systems like text processing, speech recognition, image processing. Neural networks consist of input and output layers, in many cases hidden layer consisting of units that transform the input into something that the output layer can use. They are essential tools for finding patterns which are far too complex or numerous for a human programmer to extract and teach the machine to recognize

Vigle: A Visual Graphical Learning Module on Optical Character Recognition

Students of the Arts and Humanities use OCR to convert scanned images of old text (pre-1800 AD). They need to know how digital text is extracted from the scanned image. Thanks to cell phones and images captured with them, understanding this is useful for everybody. The processing steps employed by a typical OCR software are, in order, Binarization, Deskew, Segmentation, Character Segmentation and Character Recognition. In this research project, a standalone asynchronous visual graphical learning environment (VIGLE) on Optical Character Recognition (OCR) was developed. Constructivist learning strategy was employed. The learning module was integrated into a website that works on mobile. The project attempts to generalize the instruction so that it is useful for everybody. Latest web technology was used for the implementation to achieve one stop interface, browser compatibility, responsive window sizing and interactive visual content. Binarization, Deskew and Segmentation modules were implemented in the time available. The VIGLE consists of a graphical representation and a visual interface to the lessons. Results show that the participants found both the graphical representation and the visual interface helpful. They found the incomplete learning module on OCR at best moderately useful in helping them digitize text

A Late Multi-Modal Fusion Model for Detecting Hybrid Spam E-mail

In recent years, spammers are now trying to obfuscate their intents by introducing hybrid spam e-mail combining both image and text parts, which is more challenging to detect in comparison to e-mails containing text or image only. The motivation behind this research is to design an effective approach filtering out hybrid spam e-mails to avoid situations where traditional text-based or image-baesd only filters fail to detect hybrid spam e-mails. To the best of our knowledge, a few studies have been conducted with the goal of detecting hybrid spam e-mails. Ordinarily, Optical Character Recognition (OCR) technology is used to eliminate the image parts of spam by transforming images into text. However, the research questions are that although OCR scanning is a very successful technique in processing text-and-image hybrid spam, it is not an effective solution for dealing with huge quantities due to the CPU power required and the execution time it takes to scan e-mail files. And the OCR techniques are not always reliable in the transformation processes. To address such problems, we propose new late multi-modal fusion training frameworks for a text-and-image hybrid spam e-mail filtering system compared to the classical early fusion detection frameworks based on the OCR method. Convolutional Neural Network (CNN) and Continuous Bag of Words were implemented to extract features from image and text parts of hybrid spam respectively, whereas generated features were fed to sigmoid layer and Machine Learning based classifiers including Random Forest (RF), Decision Tree (DT), Naive Bayes (NB) and Support Vector Machine (SVM) to determine the e-mail ham or spam.Comment: Accepted by 2023 the 2nd International Conference on Mechatronics and Electrical Engineering (MEEE 2023