99 research outputs found
Comparative analysis of Tesseract and Google Cloud Vision for Thai vehicle registration certificate
Optical character recognition (OCR) is a technology to digitize a paper-based document to digital form. This research studies the extraction of the characters from a Thai vehicle registration certificate via a Google Cloud Vision API and a Tesseract OCR. The recognition performance of both OCR APIs is also examined. The 84 color image files comprised three image sizes/resolutions and five image characteristics. For suitable image type comparison, the greyscale and binary image are converted from color images. Furthermore, the three pre-processing techniques, sharpening, contrast adjustment, and brightness adjustment, are also applied to enhance the quality of image before applying the two OCR APIs. The recognition performance was evaluated in terms of accuracy and readability. The results showed that the Google Cloud Vision API works well for the Thai vehicle registration certificate with an accuracy of 84.43%, whereas the Tesseract OCR showed an accuracy of 47.02%. The highest accuracy came from the color image with 1024×768 px, 300dpi, and using sharpening and brightness adjustment as pre-processing techniques. In terms of readability, the Google Cloud Vision API has more readability than the Tesseract. The proposed conditions facilitate the possibility of the implementation for Thai vehicle registration certificate recognition system
The Wiltshire Wills Feasibility Study
The Wiltshire and Swindon Record Office has nearly ninety thousand wills in its care. These records are neither adequately catalogued nor secured against loss by facsimile microfilm copies. With support from the Heritage Lottery Fund the Record Office has begun to produce suitable finding aids for the material. Beginning with this feasibility study the Record Office is developing a strategy to ensure the that facsimiles to protect the collection against risk of loss or damage and to improve public access are created.<p></p>
This feasibility study explores the different methodologies that can be used to assist the preservation and conservation of the collection and improve public access to it. The study aims to produce a strategy that will enable the Record Office to create digital facsimiles of the Wills in its care for access purposes and to also create preservation quality microfilms. The strategy aims to seek the most cost effective and time efficient approach to the problem and identifies ways to optimise the processes by drawing on the experience of other similar projects. This report provides a set of guidelines and recommendations to ensure the best use of the resources available for to provide the most robust preservation strategy and to ensure that future access to the Wills as an information resource can be flexible, both local and remote, and sustainable
Forget-free Continual Learning with Soft-Winning SubNetworks
Inspired by Regularized Lottery Ticket Hypothesis (RLTH), which states that
competitive smooth (non-binary) subnetworks exist within a dense network in
continual learning tasks, we investigate two proposed architecture-based
continual learning methods which sequentially learn and select adaptive binary-
(WSN) and non-binary Soft-Subnetworks (SoftNet) for each task. WSN and SoftNet
jointly learn the regularized model weights and task-adaptive non-binary masks
of subnetworks associated with each task whilst attempting to select a small
set of weights to be activated (winning ticket) by reusing weights of the prior
subnetworks. Our proposed WSN and SoftNet are inherently immune to catastrophic
forgetting as each selected subnetwork model does not infringe upon other
subnetworks in Task Incremental Learning (TIL). In TIL, binary masks spawned
per winning ticket are encoded into one N-bit binary digit mask, then
compressed using Huffman coding for a sub-linear increase in network capacity
to the number of tasks. Surprisingly, in the inference step, SoftNet generated
by injecting small noises to the backgrounds of acquired WSN (holding the
foregrounds of WSN) provides excellent forward transfer power for future tasks
in TIL. SoftNet shows its effectiveness over WSN in regularizing parameters to
tackle the overfitting, to a few examples in Few-shot Class Incremental
Learning (FSCIL).Comment: arXiv admin note: text overlap with arXiv:2209.0752
Advanced document data extraction techniques to improve supply chain performance
In this thesis, a novel machine learning technique to extract text-based information from scanned images has been developed. This information extraction is performed in the context of scanned invoices and bills used in financial transactions. These financial transactions contain a considerable amount of data that must be extracted, refined, and stored digitally before it can be used for analysis. Converting this data into a digital format is often a time-consuming process. Automation and data optimisation show promise as methods for reducing the time required and the cost of Supply Chain Management (SCM) processes, especially Supplier Invoice Management (SIM), Financial Supply Chain Management (FSCM) and Supply Chain procurement processes. This thesis uses a cross-disciplinary approach involving Computer Science and Operational Management to explore the benefit of automated invoice data extraction in business and its impact on SCM. The study adopts a multimethod approach based on empirical research, surveys, and interviews performed on selected companies.The expert system developed in this thesis focuses on two distinct areas of research: Text/Object Detection and Text Extraction. For Text/Object Detection, the Faster R-CNN model was analysed. While this model yields outstanding results in terms of object detection, it is limited by poor performance when image quality is low. The Generative Adversarial Network (GAN) model is proposed in response to this limitation. The GAN model is a generator network that is implemented with the help of the Faster R-CNN model and a discriminator that relies on PatchGAN. The output of the GAN model is text data with bonding boxes. For text extraction from the bounding box, a novel data extraction framework consisting of various processes including XML processing in case of existing OCR engine, bounding box pre-processing, text clean up, OCR error correction, spell check, type check, pattern-based matching, and finally, a learning mechanism for automatizing future data extraction was designed. Whichever fields the system can extract successfully are provided in key-value format.The efficiency of the proposed system was validated using existing datasets such as SROIE and VATI. Real-time data was validated using invoices that were collected by two companies that provide invoice automation services in various countries. Currently, these scanned invoices are sent to an OCR system such as OmniPage, Tesseract, or ABBYY FRE to extract text blocks and later, a rule-based engine is used to extract relevant data. While the system’s methodology is robust, the companies surveyed were not satisfied with its accuracy. Thus, they sought out new, optimized solutions. To confirm the results, the engines were used to return XML-based files with text and metadata identified. The output XML data was then fed into this new system for information extraction. This system uses the existing OCR engine and a novel, self-adaptive, learning-based OCR engine. This new engine is based on the GAN model for better text identification. Experiments were conducted on various invoice formats to further test and refine its extraction capabilities. For cost optimisation and the analysis of spend classification, additional data were provided by another company in London that holds expertise in reducing their clients' procurement costs. This data was fed into our system to get a deeper level of spend classification and categorisation. This helped the company to reduce its reliance on human effort and allowed for greater efficiency in comparison with the process of performing similar tasks manually using excel sheets and Business Intelligence (BI) tools.The intention behind the development of this novel methodology was twofold. First, to test and develop a novel solution that does not depend on any specific OCR technology. Second, to increase the information extraction accuracy factor over that of existing methodologies. Finally, it evaluates the real-world need for the system and the impact it would have on SCM. This newly developed method is generic and can extract text from any given invoice, making it a valuable tool for optimizing SCM. In addition, the system uses a template-matching approach to ensure the quality of the extracted information
Методичні вказівки до практичних занять та самостійної роботи з розвитку умінь та навичок професійного спілкування англійською мовою
Дані методичні вказівки призначені для аудиторних занять з іноземної мови під керівництвом викладача, а також можуть використовуватися для самостійного опрацювання. Методичні вказівки спрямовані на оволодіння необхідним граматичним та лексичним матеріалом з англійської мови. Робота складається з двох розділів, в яких подано сучасні тексти з наукових видань за спеціальностями комп’ютерних та інформаційних технологій, що дозволяють використовувати методичні вказівки для повторення вивченого матеріалу, для самостійної роботи з текстами в аудиторії та вдом
8th annual reality CLE
Meeting proceedings of a seminar by the same name, held October 29-30, 2020
Dynamic block encryption with self-authenticating key exchange
One of the greatest challenges facing cryptographers is the mechanism used
for key exchange. When secret data is transmitted, the chances are that there
may be an attacker who will try to intercept and decrypt the message. Having
done so, he/she might just gain advantage over the information obtained, or
attempt to tamper with the message, and thus, misguiding the recipient.
Both cases are equally fatal and may cause great harm as a consequence.
In cryptography, there are two commonly used methods of exchanging secret
keys between parties. In the first method, symmetric cryptography, the key is
sent in advance, over some secure channel, which only the intended recipient
can read. The second method of key sharing is by using a public key exchange
method, where each party has a private and public key, a public key is shared
and a private key is kept locally. In both cases, keys are exchanged between
two parties.
In this thesis, we propose a method whereby the risk of exchanging keys
is minimised. The key is embedded in the encrypted text using a process
that we call `chirp coding', and recovered by the recipient using a process
that is based on correlation. The `chirp coding parameters' are exchanged
between users by employing a USB flash memory retained by each user. If the
keys are compromised they are still not usable because an attacker can only
have access to part of the key. Alternatively, the software can be configured
to operate in a one time parameter mode, in this mode, the parameters
are agreed upon in advance. There is no parameter exchange during file
transmission, except, of course, the key embedded in ciphertext.
The thesis also introduces a method of encryption which utilises dynamic blocks, where the block size is different for each block. Prime numbers are
used to drive two random number generators: a Linear Congruential Generator
(LCG) which takes in the seed and initialises the system and a Blum-Blum
Shum (BBS) generator which is used to generate random streams to encrypt
messages, images or video clips for example. In each case, the key created is
text dependent and therefore will change as each message is sent.
The scheme presented in this research is composed of five basic modules. The
first module is the key generation module, where the key to be generated is
message dependent. The second module, encryption module, performs data
encryption. The third module, key exchange module, embeds the key into
the encrypted text. Once this is done, the message is transmitted and the
recipient uses the key extraction module to retrieve the key and finally the
decryption module is executed to decrypt the message and authenticate it.
In addition, the message may be compressed before encryption and decompressed
by the recipient after decryption using standard compression tools
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