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

Few-shot image classification : current status and research trends

Conventional image classification methods usually require a large number of training samples for the training model. However, in practical scenarios, the amount of available sample data is often insufficient, which easily leads to overfitting in network construction. Few-shot learning provides an effective solution to this problem and has been a hot research topic. This paper provides an intensive survey on the state-of-the-art techniques in image classification based on few-shot learning. According to the different deep learning mechanisms, the existing algorithms are di-vided into four categories: transfer learning based, meta-learning based, data augmentation based, and multimodal based methods. Transfer learning based methods transfer useful prior knowledge from the source domain to the target domain. Meta-learning based methods employ past prior knowledge to guide the learning of new tasks. Data augmentation based methods expand the amount of sample data with auxiliary information. Multimodal based methods use the information of the auxiliary modal to facilitate the implementation of image classification tasks. This paper also summarizes the few-shot image datasets available in the literature, and experimental results tested by some representative algorithms are provided to compare their performance and analyze their pros and cons. In addition, the application of existing research outcomes on few-shot image classification in different practical fields are discussed. Finally, a few future research directions are iden-tified. © 2022 by the authors. Licensee MDPI, Basel, Switzerland

Dark Web Activity Classification Using Deep Learning

In contemporary times, people rely heavily on the internet and search engines to obtain information, either directly or indirectly. However, the information accessible to users constitutes merely 4% of the overall information present on the internet, which is commonly known as the surface web. The remaining information that eludes search engines is called the deep web. The deep web encompasses deliberately hidden information, such as personal email accounts, social media accounts, online banking accounts, and other confidential data. The deep web contains several critical applications, including databases of universities, banks, and civil records, which are off-limits and illegal to access. The dark web is a subset of the deep web that provides an ideal platform for criminals and smugglers to engage in illicit activities, such as drug trafficking, weapon smuggling, selling stolen bank cards, and money laundering. In this article, we propose a search engine that employs deep learning to detect the titles of activities on the dark web. We focus on five categories of activities, including drug trading, weapon trading, selling stolen bank cards, selling fake IDs, and selling illegal currencies. Our aim is to extract relevant images from websites with a ".onion" extension and identify the titles of websites without images by extracting keywords from the text of the pages. Furthermore, we introduce a dataset of images called Darkoob, which we have gathered and used to evaluate our proposed method. Our experimental results demonstrate that the proposed method achieves an accuracy rate of 94% on the test dataset.Comment: 11 pages , 16 figures , 2 tables , New Dataset For DarkWeb Activity Classificatio

An integrated grammar-based approach for mathematical expression recognition

This is the author’s version of a work that was accepted for publication in Pattern Recognition. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Pattern Recognition 51 (2016) 135–147. DOI 10.1016/j.patcog.2015.09.013.Automatic recognition of mathematical expressions is a challenging pattern recognition problem since there are many ambiguities at different levels. On the one hand, the recognition of the symbols of the mathematical expression. On the other hand, the detection of the two-dimensional structure that relates the symbols and represents the math expression. These problems are closely related since symbol recognition is influenced by the structure of the expression, while the structure strongly depends on the symbols that are recognized. For these reasons, we present an integrated approach that combines several stochastic sources of information and is able to globally determine the most likely expression. This way, symbol segmentation, symbol recognition and structural analysis are simultaneously optimized. In this paper we define the statistical framework of a model based on two-dimensional grammars and its associated parsing algorithm. Since the search space is too large, restrictions are introduced for making the search feasible. We have developed a system that implements this approach and we report results on the large public dataset of the CROHME international competition. This approach significantly outperforms other proposals and was awarded best system using only the training dataset of the competition. (C) 2015 Elsevier Ltd. All rights reserved.This work was partially supported by the Spanish MINECO under the STraDA research project (TIN2012-37475-C02-01) and the FPU Grant (AP2009-4363).Álvaro Muñoz, F.; Sánchez Peiró, JA.; Benedí Ruiz, JM. (2016). An integrated grammar-based approach for mathematical expression recognition. Pattern Recognition. 51:135-147. https://doi.org/10.1016/j.patcog.2015.09.013S1351475

Community detection in networks via nonlinear modularity eigenvectors

Revealing a community structure in a network or dataset is a central problem arising in many scientific areas. The modularity function $Q$ is an established measure quantifying the quality of a community, being identified as a set of nodes having high modularity. In our terminology, a set of nodes with positive modularity is called a \textit{module} and a set that maximizes $Q$ is thus called \textit{leading module}. Finding a leading module in a network is an important task, however the dimension of real-world problems makes the maximization of $Q$ unfeasible. This poses the need of approximation techniques which are typically based on a linear relaxation of $Q$, induced by the spectrum of the modularity matrix $M$. In this work we propose a nonlinear relaxation which is instead based on the spectrum of a nonlinear modularity operator $\mathcal M$. We show that extremal eigenvalues of $\mathcal M$ provide an exact relaxation of the modularity measure $Q$, however at the price of being more challenging to be computed than those of $M$. Thus we extend the work made on nonlinear Laplacians, by proposing a computational scheme, named \textit{generalized RatioDCA}, to address such extremal eigenvalues. We show monotonic ascent and convergence of the method. We finally apply the new method to several synthetic and real-world data sets, showing both effectiveness of the model and performance of the method