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

Fine-tuning a transformers-based model to extract relevant fields from invoices

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceExtraction of relevant fields from documents has been a relevant matter for decades. Although there are well-established algorithms to perform this task since the late XX century, this field of study has again gathered more attention with the fast growth of deep learning models and transfer learning. One of these models is LayoutLM, which is a Transformer-based architecture pre-trained with additional features that represent the 2D position of the words. In this dissertation, LayoutLM is fine-tuned on a set of invoices to extract some of its relevant fields, such as company name, address, document date, among others. Given the objective of deploying the model in a company’s internal accountant software, an end-to-end machine learning pipeline is presented. The training layer receives batches with images of documents and their corresponding annotations and fine-tunes the model for a sequence labeling task. The production layer inputs images and predicts the relevant fields. The images are pre-processed extracting the whole document text and bounding boxes using OCR. To automatically label the samples using Transformers-based input format, the text is labeled using an algorithm that searches parts of the text equal or highly similar to the annotations. Also, a new dataset to support this work is created and made publicly available. The dataset consists of 813 pictures and the annotation text for every relevant field, which include company name, company address, document date, document number, buyer tax number, seller tax number, total amount and tax amount. The models are fine-tuned and compared with two baseline models, showing a performance very close to the presented by the model authors. A sensitivity analysis is made to understand the impact of two datasets with different characteristics. In addition, the learning curves for different datasets define empirically that 100 to 200 samples are enough to fine-tune the model and achieve top performance. Based on the results, a strategy for model deployment is defined. Empirical results show that the already fine-tuned model is enough to guarantee top performance in production without the need of using online learning algorithms