Background.Identifying the quality of the products in the manufacturing industry is a challenging task. Manufacturers use needles to print unique numbers on the products to differentiate between good and bad quality products. However, identi- fying these needle printed characters can be difficult. Hence, new technologies like deep learning and optical character recognition (OCR) are used to identify these characters. Objective.The primary ob jective of this thesis is to identify the needle-printed characters on steel bars. This ob jective is divided into two sub-ob jectives. The first sub-ob jective is to identify the region of interest on the steel bars and extract it from the images. The second sub-ob jective is to identify the characters on the steel bars from the extracted images. The YOLOV5 and YOLOV5-obb ob ject detection algorithms are used to achieve these ob jectives. Method. Literature review was performed at first to select the algorithms, then the research was to collect the dataset, which was provided by OVAKO. The dataset included 1000 old images and 3000 new images of steel bars. To answer the RQ2, at first existing OCR techniques were used on the old images which had low accuracy levels. So, the YOLOV5 algorithm was used on old images to detect the region of interest. Different rotation techniques are applied to the cropped images(cropped after the bounding box is detected) no promising result is observed so YOLOV5 at the character level is used in identifying the characters, the results are unsatisfactory. To achieve this, YOLOV5-obb was used on the new images, which resulted in good accuracy levels. Results. Accuracy and mAP are used to assess the performance of OCRs and selected ob ject detection algorithms. The current study proved Existing OCR was also used in the extraction, however, it had an accuracy of 0%, which implies it failed to identify characters. With a mAP of 0.95, YOLOV5 is good at extracting cropped images but fails to identify the characters. When YOLOV5-obb is used for attaining orientation, it achieves a mAP of 0.93. Due to time constraint, the last part of the thesis was not implemented. Conclusion. The present research employed YOLOV5 and YOLOV5-obb ob ject detection algorithms to identify needle-printed characters on steel bars. By first se- lecting the region of interest and then extracting images, the study ob jectives were met. Finally, character-level identification was performed on the old images using the YOLOV5 technique and on the new images using the YOLOV5-obb algorithm, with promising results

Since the Russian invasion of Ukraine on the 24th of February 2022 – most of the United Nations have, in one way or another, participated in the most significant war of many decades. The war is characterized by Russia’s atrocious war crimes, illegal annexations, terror, propaganda, and complete disrespect for international law. On the other hand, the war has also been characterized by Ukrainian resilience, a united Europe, and a new dimension of intelligence gathering through social media.Due to the internet, social media, the accessibility of mobile devices, and Ukraine’s military and civilianeffort in documenting Russian equipment – its whereabouts, status, and quantity, Open-Source Intelligence possibilities have reached new levels for both professionals and amateurs. Despite these improved possibilities, gathering such a vast amount of data is still a Herculean effort.Hence, this study contributes a starting point for anyone wanting to compile equipment losses by providing a process specialized in automatic data extraction and summarization from an existing database. The database in question is the image collection from the military analysis group Oryxspioenkop. To further complement the information provided by Oryxspioenkop, the method automatically extracts and annotates dates from the images to provide a chronological order of the equipment loss as well as a graphical overview.The process shows promising results and manages to compile a large set of data, both the information provided by Oryx and the extracted dates from its imagery. Further, the automated process proves to be many times faster than its manual counterpart, showing a linear relationship between the number of images analysed and manhours saved. However, due to the limited development time – the process still has room for improvement and should be considered semi-automatic, rather than automatic. Nevertheless, thanks to the open-source design, the process can be continuously updated and modified to work with other databases, images, or the extraction of other strings of text from imagery.With the rise of competent artificial image generation models, the study also raises the question if this kind of imagery will be a reliable source in the future when studying equipment losses, or if artificial intelligence will be used as a tool of propaganda and psychological operations in wars to come. / Sedan Rysslands oprovocerade invasion av Ukraina den 24e februari 2022 – har stora delar av de Förenta nationerna engagerat sig i århundradets mest signifikanta krig. Kriget har karaktäriserats av ryska krigsbrott, olagliga annekteringar, terror, propaganda samt en total avsaknad av respekt för folkrätt. I kontrast, har kriget även karaktäriserats av Ukrainas ovillkorliga motståndskraft, ett enat Europa och en ny dimension av underrättelseinhämtning från sociala medier.Genom internet, sociala medier, tillgängligheten av mobiltelefoner och Ukrainas militära och civila ansträngning att dokumentera rysk materiel – vart den befinner sig, vilken status den har samt vilken kvantitet den finns i, har öppen underrättelseinhämtning blomstrat på både professionell och amatörnivå. Dock, på grund av den kvantitet som denna data genereras i, kräver en helhetssammanställning en oerhörd insats.Därav avser detta arbete ge en grund för sammanställning av materielförluster genom att tillhandahålla en automatiserad process för att extrahera data från en befintlig databas. Detta har exemplifierats genom att nyttja bildkollektioner från Oryxspioenkop, en grupp bestående av militäranalytiker som fokuserar på sammanställning av grafiskt material. Utöver detta så kompletterar processen befintliga data genom att inkludera datumet då materielen dokumenterats. Därigenom ges även en kronologisk ordning för förlusterna.Processen visar lovande resultat och lyckas att effektivt och träffsäkert sammanställa stora mängder data. Vidare lyckas processen att överträffa sin manuella motsvarighet och visar på ett linjärt samband mellan antalet analyserade bilder och besparade mantimmar. Dock, på grund av den korta utvecklingstiden har processen fortfarande en del utvecklingsmöjlighet och förblir semiautomatisk, snarare än automatisk. Å andra sidan, eftersom processen bygger på öppen källkod, finns fortsatt möjlighet att uppdatera och modifiera processen för att passa annat källmaterial.Slutligen, i och med den kontinuerliga utvecklingen av artificiell intelligens och artificiellt genererade bilder,lyfter studien frågan om denna typ av data kommer vara en trovärdig källa i framtida analyser av materielförluster, eller om det kommer att förvandlas till verktyg för propaganda och påverkansoperationeri ett framtida krig.

This master’s thesis is focused on anonymization of ultrasound data in DICOM format. Haar wavelet belonging to Daubechies wavelet family is used to detect text areas in the image. Extraction of the text from the image is done using a free tool - tesseract OCR Engine. Finally, detected text is compared to sensitive data from DICOM metadata using Levenshtein - edit distance algorithm.

As an effort to improve accessibility to historical documents, digitization of historical archives has been an ongoing process at many institutions since the origination of Optical Character Recognition. The old, scanned documents can contain deteriorations acquired over time or caused by old printing methods. Common visual attributes seen on the documents are variations in style and font, broken characters, ink intensity, noise levels and damage caused by folding or ripping and more. Many of these attributes are disfavoring for modern Optical Character Recognition tools and can lead to failed character recognition. This study approaches stated problem by using image processing methods to improve the result of character recognition. Furthermore, common image quality characteristics of scanned historical documents with unidentifiable text are analyzed. The Optical Character Recognition tool used to conduct this research was the open-source Tesseract software. Image processing methods like Gaussian lowpass filtering, Otsu’s optimum thresholding method and morphological operations were used to prepare the historical documents for Tesseract. Using the Precision and Recall classification method, the OCR output was evaluated, and it was seen that the recall improved by 63 percentage points and the precision by 18 percentage points. This shows that using image pre-processing methods as an approach to increase the readability of historical documents for Optical Character Recognition tools is effective. Further it was seen that common characteristics that are especially disadvantageous for Tesseract are font deviations, occurrence of non-belonging objects, character fading, broken characters, and Poisson noise.

This master’s thesis deals with optical character recognition. The first part describes the basic types of optical character recognition tasks and divides algorithm into individual phases. For each phase the most commonly used methods are described in the next part. Within the character recognition phase the problematics of artificial neural networks and their usage in given phase is explained, specifically multilayer perceptron and convolutional neural networks. The second part deals with requirements definition for specific application to be used as feedback for robotic system. Convolution neural networks and CNTK library for deep learning using algorithm implementation in .NET is introduced. Finally, the test results of the individual phases of the proposed solution and the comparison with the open source Tesseract engine are discussed.

The progressive metal style "djent" emerged in the mid-to-late 2000s with bands that modeled their use of extended range instruments and complex rhythmic cycles after that of Swedish metal band Meshuggah. The addition of a new vocabulary of melody and harmony by bands such as Periphery, Tesseract, and Animals as Leaders has come to define djent in a new way and provided fruitful ground for voice-leading and metrical analysis. In this dissertation, I approach analysis in two steps. The first step is the production of detailed transcriptions of four djent songs. The process of transcription has allowed for the development of Transcription Preference Rules, modeled after Lerdahl and Jackendoff's preference rule approach in their Generative Theory of Tonal Music. The Transcription Preference Rules account for the selection of key signatures, time signatures, and other features of the scores that may affect analysis. Second, using these scores, I examine the connection between the textual topic of change and the voice-leading and metrical structures in Periphery's "Insomnia" and Tesseract's "Of Matter." I show how this topic is reflected by techniques such as change melodic direction, multidimensional metrical dissonance, and auxiliary cadential events. Finally, I apply voice-leading and metrical analysis to Animals as Leaders's "Tempting Time" and Mute the Saint's "Sound of Scars" in order to show what these analytical techniques reveal about instrumental djent pieces. I show how shifts in meter in "Tempting Time" can be represented cyclically. I conclude by showing how the interaction of metal and North Indian Classical techniques produces a unique representation of Mute the Saint's topic of longing and frustration in "Sound of Scars."

License plate recognition (LPR) system plays an important role in numerous applications, such as parking accounting systems, traffic law enforcement, road monitoring, expressway toll system, electronic-police system, and security systems. In recent years, there has been a lot of research in license plate recognition, and many recognition systems have been proposed and used. But these systems have been developed for computers. In this project, we developed a mobile LPR system for Android Operating System (OS). LPR involves three main components: license plate detection, character segmentation and Optical Character Recognition (OCR). For License Plate Detection and character segmentation, we used JavaCV and OpenCV libraries. And for OCR, we used tesseract-ocr. We obtained very good results by using these libraries. We also stored records of license numbers in database and for that purpose SQLite has been used.

The thesis is concerned with optical character recognition from image data with different methods used for character classification. In the first theoretical part it focuses on explanation of all important parts of system for optical character recognition. The latter practical part of the thesis describes an example of image segmentation, the implementation of artificial neural networks for image recognition and create simple training set of data for the evaluation of the network. It also describes the process of training Tesseract tool and its implementation in a simple application EasyTessOCR for character recognition.

This thesis describes a development of a modern client-server application for text recognition on iOS platform. The reader is acquainted with common principles of a client-server model, including its known architecture styles, and with a distribution of logical layers between both sides of the model. After that the thesis depicts current trends and examples of suitable technologies for creating an application programming interface of a web server. Possible ways of text recognition on the server side are discussed as well. In context of a client side, the thesis provides an insight into iOS platform and a few important concepts in iOS application development. Following implementation of the server side is stressed to be reusable as much as possible for different kinds of use cases. Last but not least, the thesis provides a simple iOS framework for a direct communication with the recognition server. Finally, an application for evaluation of food ingredients from a packaging material is implemented as an example of usage.

This thesis aims at creation of new datasets for text recognition machine learning tasks and experiments with convolutional neural networks on these datasets. It describes architecture of convolutional nets, difficulties of recognizing text from photographs and contemporary works using these networks. Next, creation of annotation, using Tesseract OCR, for dataset comprised from photos of document pages, taken by mobile phones, named Mobile Page Photos. From this dataset two additional are created by cropping characters out of its photos formatted as Street View House Numbers dataset. Dataset Mobile Nice Page Photos Characters contains readable characters and Mobile Page Photos Characters adds hardly readable and unreadable ones. Three models of convolutional nets are created and used for text recognition experiments on these datasets, which are also used for estimation of annotation error.