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

Off-Line Handwritten Arabic Characters Segmentation Using Slant-Tolerant Segment Features (STSF) [PJ6123. S562 2007 f rb].

Tema utama bagi kajian ini ialah pensegmenan aksara tulisan Arab luar talian. Suatu sistem pengecaman aksara tulisan Arab yang baik mampu meningkatkan kesalingtindakan antara manusia dengan komputer. The main theme of this research is the off-line handwritten Arabic characters segmentation. A successful handwritten Arabic character recognition system improves interactivity between the human and the computers

Issues & Challenges in Urdu OCR

Optical character recognition is a technique that is used to recognized printed and handwritten text into editable text format. There has been a lot of work done through this technology in identifying characters of different languages with variety of scripts. In which Latin scripts with isolated characters (non-cursive) like English are easy to recognize and significant advances have been made in the recognition; whereas, Arabic and its related cursive languages like Urdu have more complicated and intermingled scripts, are not much worked. This paper discusses a detail of various scripts of Urdu language also discuss issues and challenges regarding Urdu OCR. due to its cursive nature which include cursiveness, more characters dots, large set of characters for recognition, more base shape group characters, placement of dots, ambiguity between the characters and ligatures with very slight difference, context sensitive shapes, ligatures, noise, skew and fonts in Urdu OCR. This paper provides a better understanding toward all the possible engendering dilemmas related to Urdu character recognition

Deep Sparse Auto-Encoder Features Learning for Arabic Text Recognition

One of the most recent challenging issues of pattern recognition and artificial intelligence is Arabic text recognition. This research topic is still a pervasive and unaddressed research field, because of several factors. Complications arise due to the cursive nature of the Arabic writing, character similarities, unlimited vocabulary, use of multi-size and mixed-fonts, etc. To handle these challenges, an automatic Arabic text recognition requires building a robust system by computing discriminative features and applying a rigorous classifier together to achieve an improved performance. In this work, we introduce a new deep learning based system that recognizes Arabic text contained in images. We propose a novel hybrid network, combining a Bag-of-Feature (BoF) framework for feature extraction based on a deep Sparse Auto-Encoder (SAE), and Hidden Markov Models (HMMs), for sequence recognition. Our proposed system, termed BoF-deep SAE-HMM, is tested on four datasets, namely the printed Arabic line images Printed KHATT (P-KHATT), the benchmark printed word images Arabic Printed Text Image (APTI), the benchmark handwritten Arabic word images IFN/ENIT, and the benchmark handwritten digits images Modified National Institute of Standards and Technology (MNIST)

SCyDia – OCR For Serbian Cyrillic with Diacritics

In the currently ongoing process of retro-digitization of Serbian dialectal dictionaries, the biggest obstacle is the lack of machine-readable versions of paper editions. Therefore, one essential step is needed before venturing into the dictionary-making process in the digital environment – OCRing the pages with the highest possible accuracy. Successful retro-digitization of Serbian dialectal dictionaries, currently in progress, has shown a dire need for one basic yet necessary step, lacking until now – OCRing the pages with the highest possible accuracy. OCR processing is not a new technology, as many opensource and commercial software solutions can reliably convert scanned images of paper documents into digital documents. Available software solutions are usually efficient enough to process scanned contracts, invoices, financial statements, newspapers, and books. In cases where it is necessary to process documents that contain accented text and precisely extract each character with diacritics, such software solutions are not efficient enough. This paper presents the OCR software called “SCyDia”, developed to overcome this issue. We demonstrate the organizational structure of the OCR software “SCyDia” and the first results. The “SCyDia” is a web-based software solution that relies on the open-source software “Tesseract” in the background. “SCyDia” also contains a module for semi-automatic text correction. We have already processed over 15,000 pages, 13 dialectal dictionaries, and five dialectal monographs. At this point in our project, we have analyzed the accuracy of the “SCyDia” by processing 13 dialectal dictionaries. The results were analyzed manually by an expert who examined a number of randomly selected pages from each dictionary. The preliminary results show great promise, spanning from 97.19% to 99.87%

SCyDia – OCR For Serbian Cyrillic with Diacritics

In the currently ongoing process of retro-digitization of Serbian dialectal dictionaries, the biggest obstacle is the lack of machine-readable versions of paper editions. Therefore, one essential step is needed before venturing into the dictionary-making process in the digital environment – OCRing the pages with the highest possible accuracy. Successful retro-digitization of Serbian dialectal dictionaries, currently in progress, has shown a dire need for one basic yet necessary step, lacking until now – OCRing the pages with the highest possible accuracy. OCR processing is not a new technology, as many opensource and commercial software solutions can reliably convert scanned images of paper documents into digital documents. Available software solutions are usually efficient enough to process scanned contracts, invoices, financial statements, newspapers, and books. In cases where it is necessary to process documents that contain accented text and precisely extract each character with diacritics, such software solutions are not efficient enough. This paper presents the OCR software called “SCyDia”, developed to overcome this issue. We demonstrate the organizational structure of the OCR software “SCyDia” and the first results. The “SCyDia” is a web-based software solution that relies on the open-source software “Tesseract” in the background. “SCyDia” also contains a module for semi-automatic text correction. We have already processed over 15,000 pages, 13 dialectal dictionaries, and five dialectal monographs. At this point in our project, we have analyzed the accuracy of the “SCyDia” by processing 13 dialectal dictionaries. The results were analyzed manually by an expert who examined a number of randomly selected pages from each dictionary. The preliminary results show great promise, spanning from 97.19% to 99.87%

Efficient Machine Learning Methods for Document Image Analysis

With the exponential growth in volume of multimedia content on the internet, there has been an increasing interest for developing more efficient and scalable algorithms to learn directly from data without excessive restrictions on nature of the content. In the context of document images, many large scale digitization projects have called for reliable and scalable triage methods for enhancement, segmentation, grouping and categorization of captured images. Current approaches, however, are typically limited to a specific class of documents such as scanned books, newspapers, journal articles or forms for example, and analysis and processing of more unconstrained and noisy heterogeneous document collections has not been as widely addressed. Additionally, existing machine-learning based approaches for document processing need to be carefully applied to handle the challenges associated with large and imbalanced training data. In this thesis, we address these challenges in three primary applications of document image analysis - low-level document enhancement, mid-level handwritten line segmentation, and high-level classification and retrieval. We first present a data selection method for training Support Vector Machines (SVM) on large-scale data sets. We apply the proposed approach to pixel-level document image enhancement, and show promising results with a relatively small number of training samples. Second, we present a graph-based method for segmentation of handwritten document images into text-lines which is more efficient and adaptive than previous approaches. Our approach demonstrates that combining results from local and global methods enhances the final performance of text-line segmentation. Third, we present an approach to compute structural similarities between images for classification and retrieval. Results on real-world data sets show that the approach is more effective than earlier approaches when the labeled data is limited. We extend our classification approach to a completely unsupervised setting, where both the number of classes and representative samples from each class is assumed to be unknown. We present a method for computing similarities based on learned structural patterns and correlations from the given data. Experiments with four different data sets show that our approach can estimate number of classes in large document collections and group structurally similar images with a high-accuracy

COVID-19 and Organized Crime: Strategies employed by criminal groups to increase their profits and power in the first months of the pandemic

The COVID-19 pandemic has created new opportunities for organized criminal groups and confronted them with new challenges. Analysis of how these groups have reacted to the pandemic yields better understanding of how they work and enables the devising of more effective counter-strategies. To this end, we identified illustrative cases regarding the provision of illegal governance and infiltration of the legal economy by conducting a systematic content analysis of international media articles and institutional reports published during the first eight months after the outbreak of the pandemic (January to August 2020). These cases were further analyzed in order to cluster the behavior of criminal groups in response to the COVID-19 emergency, and the means by which they tried to exploit the pandemic to strengthen their political and economic power. We found that different governance-type criminal groups proposed themselves as institutions able to mitigate the burdens imposed by the pandemic by providing support to people in need and enforcing social-distancing measures. Further, identified cases did not provide evidence of groups devoted to the provision of illicit services and goods assuming any governance role. In this respect, the available evidence supports previous knowledge about organized crime. Cases of misappropriation of public funds and organized crime infiltration of the legal economy seem less common, at least in the first phase of the pandemic. The wholesale distribution of pharmaceuticals and medicines has been the sector targeted the most