Incremental classification of invoice documents

ISBN : 978-1-4244-2174-9International audienceThis paper deals with incremental classification and its particular application to invoice classification. An improved version of an already existant incremental neural network called IGNG (Incremental Growing Neural Gas) is used for this purpose . This neural network tries to cover the space of data by adding or deleting neurons as data is fed to the system. The improved version of the IGNG, called I2GNG used local thresholds in order to create or delete neurons. Applied on invoice documents represented with graphs, I2GNG shows a recognition rate of 97.63%

CloudScan - A configuration-free invoice analysis system using recurrent neural networks

We present CloudScan; an invoice analysis system that requires zero configuration or upfront annotation. In contrast to previous work, CloudScan does not rely on templates of invoice layout, instead it learns a single global model of invoices that naturally generalizes to unseen invoice layouts. The model is trained using data automatically extracted from end-user provided feedback. This automatic training data extraction removes the requirement for users to annotate the data precisely. We describe a recurrent neural network model that can capture long range context and compare it to a baseline logistic regression model corresponding to the current CloudScan production system. We train and evaluate the system on 8 important fields using a dataset of 326,471 invoices. The recurrent neural network and baseline model achieve 0.891 and 0.887 average F1 scores respectively on seen invoice layouts. For the harder task of unseen invoice layouts, the recurrent neural network model outperforms the baseline with 0.840 average F1 compared to 0.788.Comment: Presented at ICDAR 201

Multi-domain document layout understanding using few-shot object detection

We try to address the problem of document layout understanding using a simple algorithm which generalizes across multiple domains while training on just few examples per domain. We approach this problem via supervised object detection method and propose a methodology to overcome the requirement of large datasets. We use the concept of transfer learning by pre-training our object detector on a simple artificial (source) dataset and fine-tuning it on a tiny domain specific (target) dataset. We show that this methodology works for multiple domains with training samples as less as 10 documents. We demonstrate the effect of each component of the methodology in the end result and show the superiority of this methodology over simple object detectors. We will open-source the code, trained models, source and target datasets upon acceptance

Factors that influence late payments in government new build infrastructure projects in Gauteng Province, South Africa

A research report submitted in partial fulfillment of the requirements for the degree of Master of Science in Building to the Faculty of Engineering and the Built Environment, School of Architecture and Planning at the University of the Witwatersrand, Johannesburg, 2017Payments are an essential component of construction contracts (Murdoch and Hughs, 2015). The nature of the payment regime has an effect on the contractor’s cash flow, project performance and therefore achievement of project objectives. Finance has been claimed as the most important resource in the construction process (Mawdesley et al., 1997). As such, proper financial planning to ensure healthy cash flow during the lifespan of a project is central to its performance and ultimate success. The obligation of an employer is to pay the contractor timeously, as per agreed payment plan and likewise the obligation of a contractor is to produce the build works according to an agreed schedule of works and to set standards of quality. Project finance in its totality is therefore of major importance to the progression of the construction process. Purpose - The main purpose of this study is to identify current problems in relation to late payment issues encountered by contractors that have been commissioned to construct public infrastructure for the government of South Africa. The paper seeks to highlight the extent of occurrence, to measure, and to assess the extent of late payment, in public infrastructure projects in the Gauteng Province. This study is done with a view to study the correlative relationship between the deviance in contractually scheduled payment time and time of actual payment and to identify factors that influence these. Design methodology and approach – With regard to research methods, the study adopts a mixed approach. Both Qualitative and Quantitative approaches to the study were adopted. This was done by collecting data through structured questionnaires and the research instruments administered to key personnel in the various Sector Departments as well as the Finance Department and the Gauteng Department of Infrastructure Development. Sample invoices were gleaned and scrutinized from 189 projects with regard to invoice payment data. Respondents were asked to provide information on Invoice Date, Date Invoice Received By Department and Date Invoice Paid On. This information was gathered for approximately half the total number of invoices received per project. Adopting a project by project approach, respondents were asked to provide data and causal factors linked to late payment. The results were then analyzed to determine significant relationships between late payment patterns and the factors that influence these.XL201