Automatic Multiple Choice Examination Questions Marking and Grade Generator Software

This paper discusses a feasible software solution that enables automatic marking andgrading of scripts. Technology keeps expanding, and more advanced innovations arebeing implemented with time. The marking and allocation of grades for examina-tion scripts through human efforts are gradually becoming a thing of the past. Hence,machines and software applications are introduced to make the entire marking andgrading of examination scripts more efficient, fast, and less tedious. Computer visionis an artificial intelligence (AI) knowledge domain that ensures devices obtain usefulinformation from digital images, videos, and other visual inputs. Image processingand recognition, a unique part of computer vision alongside the python program-ming language and the OpenCV library was employed for this project. These are themost used in developing most recent applications that utilize, to some extent, arti-ficial intelligence to attain specific desired results. The result of the project seeksto develop a maintainable android software application that uses image processingtechnology to scan patterns or images and grades results of multiple-choice questionscripts based on a set marking scheme. This ensures that desired results are obtainedwhile increasing efficiency and productivity

Knowledge-based document retrieval with application to TEXPROS

Document retrieval in an information system is most often accomplished through keyword search. The common technique behind keyword search is indexing. The major drawback of such a search technique is its lack of effectiveness and accuracy. It is very common in a typical keyword search over the Internet to identify hundreds or even thousands of records as the potentially desired records. However, often few of them are relevant to users\u27 interests. This dissertation presents knowledge-based document retrieval architecture with application to TEXPROS. The architecture is based on a dual document model that consists of a document type hierarchy and, a folder organization. Using the knowledge collected during document filing, the search space can be narrowed down significantly. Combining the classical text-based retrieval methods with the knowledge-based retrieval can improve tremendously both search efficiency and effectiveness. With the proposed predicate-based query language, users can more precisely and accurately specify the search criteria and their knowledge about the documents to be retrieved. To assist users formulate a query, a guided search is presented as part of an intelligent user interface. Supported by an intelligent question generator, an inference engine, a question base, and a predicate-based query composer, the guided search collects the most important information known to the user to retrieve the documents that satisfy users\u27 particular interests. A knowledge-based query processing and search engine is presented as the core component in this architecture. Algorithms are developed for the search engine to effectively and efficiently retrieve the documents that match the query. Cache is introduced to speed up the process of query refinement. Theoretical proof and performance analysis are performed to prove the efficiency and effectiveness of this knowledge-based document retrieval approach

Reflectance Transformation Imaging (RTI) System for Ancient Documentary Artefacts

This tutorial summarises our uses of reflectance transformation imaging in archaeological contexts. It introduces the UK AHRC funded project reflectance Transformation Imaging for Anciant Documentary Artefacts and demonstrates imaging methodologies

Deep Learning Based Real Time Devanagari Character Recognition

The revolutionization of the technology behind optical character recognition (OCR) has helped it to become one of those technologies that have found plenty of uses in the entire industrial space. Today, the OCR is available for several languages and have the capability to recognize the characters in real time, but there are some languages for which this technology has not developed much. All these advancements have been possible because of the introduction of concepts like artificial intelligence and deep learning. Deep Neural Networks have proven to be the best choice when it comes to a task involving recognition. There are many algorithms and models that can be used for this purpose. This project tries to implement and optimize a deep learning-based model which will be able to recognize Devanagari script’s characters in real time by analyzing the hand movements

Automated classification of receipts and invoices along with document extraction

Companies might receive dozens or even hundreds of receipts and invoices per day. It consumes a lot of working hours to keep them all organized – invoices must be paid on time and receipts must be archived properly. This research aims to reduce the amount of manual labor the organizing requires with automated classification. Personally, I’m writing this thesis in collaboration with my workplace – a company called Eneroc Ltd. They had a problem with document classification consuming too many working hours. Therefore, they created a system to automate this process. The existing system uses a text-based approach that searches for specific key words in the documents. The system works rather well, but the company wanted to find out if some modern approach could outperform the existing system and add more features into the process. The goal of this research is to find out if a machine learning based approach could be used to classify documents into invoices and receipts. In addition to the classification, the approach should also be able to collect key information from the documents. This thesis describes the workflow of creating a machine learning based solution to tackle the given challenge. The research resulted in an application that takes in invoices and receipts in PDF format. The system trains a k-nearest neighbors model with training data, that was created in the process of the research. The model is then used to classify different parts of the new PDF files into predefined categories. The key information is extracted from these categories. The k-NN model was validated with k-fold cross-validation. The validation showed that the model is performing correctly. Some preprocessing was also introduced in the process, which further improved the results. Good results with the k-NN model imply that using a proper machine learning solution would be profitable. The final classification between receipts and invoices, as well as the key information extraction, is done based on the classified document parts. This works rather well on the classification and simple key information extraction. But more complex key information extraction – like the product list extraction – still requires more work. The research proved that machine learning solution could be used to classify documents into invoices and receipts, and also to collect key information from the documents. The created application isn’t yet ready for deployment, but it gives a good foundation for future development. The research also shows which steps to take next and where to focus on when improving the system

Design of an Automated Book Reader as an Assistive Technology for Blind Persons

This dissertation introduces a novel automated book reader as an assistive technology tool for persons with blindness. The literature shows extensive work in the area of optical character recognition, but the current methodologies available for the automated reading of books or bound volumes remain inadequate and are severely constrained during document scanning or image acquisition processes. The goal of the book reader design is to automate and simplify the task of reading a book while providing a user-friendly environment with a realistic but affordable system design. This design responds to the main concerns of (a) providing a method of image acquisition that maintains the integrity of the source (b) overcoming optical character recognition errors created by inherent imaging issues such as curvature effects and barrel distortion, and (c) determining a suitable method for accurate recognition of characters that yields an interface with the ability to read from any open book with a high reading accuracy nearing 98%. This research endeavor focuses in its initial aim on the development of an assistive technology tool to help persons with blindness in the reading of books and other bound volumes. But its secondary and broader aim is to also find in this design the perfect platform for the digitization process of bound documentation in line with the mission of the Open Content Alliance (OCA), a nonprofit Alliance at making reading materials available in digital form. The theoretical perspective of this research relates to the mathematical developments that are made in order to resolve both the inherent distortions due to the properties of the camera lens and the anticipated distortions of the changing page curvature as one leafs through the book. This is evidenced by the significant increase of the recognition rate of characters and a high accuracy read-out through text to speech processing. This reasonably priced interface with its high performance results and its compatibility to any computer or laptop through universal serial bus connectors extends greatly the prospects for universal accessibility to documentation

Vision Based Extraction of Nutrition Information from Skewed Nutrition Labels

An important component of a healthy diet is the comprehension and retention of nutritional information and understanding of how different food items and nutritional constituents affect our bodies. In the U.S. and many other countries, nutritional information is primarily conveyed to consumers through nutrition labels (NLs) which can be found in all packaged food products. However, sometimes it becomes really challenging to utilize all this information available in these NLs even for consumers who are health conscious as they might not be familiar with nutritional terms or find it difficult to integrate nutritional data collection into their daily activities due to lack of time, motivation, or training. So it is essential to automate this data collection and interpretation process by integrating Computer Vision based algorithms to extract nutritional information from NLs because it improves the user’s ability to engage in continuous nutritional data collection and analysis. To make nutritional data collection more manageable and enjoyable for the users, we present a Proactive NUTrition Management System (PNUTS). PNUTS seeks to shift current research and clinical practices in nutrition management toward persuasion, automated nutritional information processing, and context-sensitive nutrition decision support. PNUTS consists of two modules, firstly a barcode scanning module which runs on smart phones and is capable of vision-based localization of One Dimensional (1D) Universal Product Code (UPC) and International Article Number (EAN) barcodes with relaxed pitch, roll, and yaw camera alignment constraints. The algorithm localizes barcodes in images by computing Dominant Orientations of Gradients (DOGs) of image segments and grouping smaller segments with similar DOGs into larger connected components. Connected components that pass given morphological criteria are marked as potential barcodes. The algorithm is implemented in a distributed, cloud-based system. The system’s front end is a smartphone application that runs on Android smartphones with Android 4.2 or higher. The system’s back end is deployed on a five node Linux cluster where images are processed. The algorithm was evaluated on a corpus of 7,545 images extracted from 506 videos of bags, bottles, boxes, and cans in a supermarket. The DOG algorithm was coupled to our in-place scanner for 1D UPC and EAN barcodes. The scanner receives from the DOG algorithm the rectangular planar dimensions of a connected component and the component’s dominant gradient orientation angle referred to as the skew angle. The scanner draws several scan lines at that skew angle within the component to recognize the barcode in place without any rotations. The scanner coupled to the localizer was tested on the same corpus of 7,545 images. Laboratory experiments indicate that the system can localize and scan barcodes of any orientation in the yaw plane, of up to 73.28 degrees in the pitch plane, and of up to 55.5 degrees in the roll plane. The videos have been made public for all interested research communities to replicate our findings or to use them in their own research. The front end Android application is available for free download at Google Play under the title of NutriGlass. This module is also coupled to a comprehensive NL database from which nutritional information can be retrieved on demand. Currently our NL database consists of more than 230,000 products. The second module of PNUTS is an algorithm whose objective is to determine the text skew angle of an NL image without constraining the angle’s magnitude. The horizontal, vertical, and diagonal matrices of the (Two Dimensional) 2D Haar Wavelet Transform are used to identify 2D points with significant intensity changes. The set of points is bounded with a minimum area rectangle whose rotation angle is the text’s skew. The algorithm’s performance is compared with the performance of five text skew detection algorithms on 1001 U.S. nutrition label images and 2200 single- and multi-column document images in multiple languages. To ensure the reproducibility of the reported results, the source code of the algorithm and the image data have been made publicly available. If the skew angle is estimated correctly, optical character recognition (OCR) techniques can be used to extract nutrition information

Integration of document representation, processing and management

This paper describes a way for document representation and proposes an approach towards an integrated document processing and management system. The approach has the intention to capture essentially freely structured documents, like those typically used in the office domain. The document analysis system ANASTASIL is capable to reveal the structure of complex paper documents, as well as logical objects within it, like receiver, footnote, date. Moreover, it facilitates the handling of the containing information. Analyzed documents are stored by the management system KRISYS that is connected to several different subsequent services. The described integrated system can be considered as an ideal extension of the human clerk, making his tasks in information processing easier. The symbolic representation of the analysis results allow an easy transformation in a given international standard, e.g., ODA/ODIF or SGML, and to interchange it via global network

Content-based image analysis with applications to the multifunction printer imaging pipeline and image databases

Image understanding is one of the most important topics for various applications. Most of image understanding studies focus on content-based approach while some others also rely on meta data of images. Image understanding includes several sub-topics such as classification, segmentation, retrieval and automatic annotation etc., which are heavily studied recently. This thesis proposes several new methods and algorithms for image classification, retrieval and automatic tag generation. The proposed algorithms have been tested and verified in multiple platforms. For image classification, our proposed method can complete classification in real-time under hardware constraints of all-in-one printer and adaptively improve itself by online learning. Another image understanding engine includes both classification and image quality analysis is designed to solve the optimal compression problem of printing system. Our proposed image retrieval algorithm can be applied to either PC or mobile device to improve the hybrid learning experience. We also develop a new matrix factorization algorithm to better recover the image meta data (tag). The proposed algorithm outperforms other existing matrix factorization methods