Utilising Machine Learning Methods in Creating Digital Equipment Information

Utilising Machine Learning Methods in Creating Digital Equipment Information

Author: Tiittanen Sara, MSc(Econ) Published: 29.10.2021

Study Paper Trail Systems Ltd

2021

Study Paper

Summary 1.

Introduction

2.

Concepts

2.1.

Optical Character Recognition OCR

2.2.

Natural Language Processing NLP

3.

Methods

3.1.

Commercial Tools

3.2.

Gathering Data

3.3.

Step 1 Optical Character Recognition

3.4.

Step 2 Classification

3.5.

Step 3 Natural Language Processing

3.6.

Step 4 Supervised Learning

4.

Results

5.

Results Analysis Sources

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Content

The aim of this study was to find out if it is possible to create digital equipment information automatically from the purchase or ordering documentation of the equipment. In particular, the study examined the effectiveness of machine learning methods, text recognition, and natural language processing in automated document processing.

From the different methods of artificial intelligence, Optical Character Regognition OCR and Natural Language Processing NLP were selected for this research project.

The aim was to understand whether it is possible to generate digital equipment information in a system-understandable format directly from different supplier-dependent documentation.

The results are promising and the methods chosen were proven to work in identifying the desired data as well as in modifying the data into a machine-readable format.

Based on the research results, it is possible to achieve both efficiency and automation in document management by utilising machine learning methods in the context of equipment management.

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Summary

Utilising Machine Learning Methods in Creating Digital Equipment Information

1. Introduction

Digital equipment information is known to generate numerous benefits for organisations Shang & Seddon 2000, Poor et al 2016 However, digitisation of information itself is often a seen as an obstacle in achieving the benefits of digital equipment management. This study addressed the challenge of digitisation by exploring the use of machine learning methods in the creation of digital asset information.

In connection with the purchase of equipment, the equipment supplier typically provides the customer with a document, order confirmation or receipt related to the order. These documents typically contain information about the equipment being purchased. Documentation related to asset orders varies in format and file format (files are typically images or PDF files).

The aim of the study was to find out whether it is possible to create new digital asset articles in the equipment management system, and to automatically fill in the data fields with automatic processing of documents based on machine learning.

Among machine learning methods, visual recognition and natural language processing were selected for processing the research material. The reason was that it was identified the data could be imported into machine-readable form by means of visual identification and extracting of any information required understanding the context of the text.

Machine vision and natural language processing have been extensively studied, but the studies regarding applications to equipment management are still limited. In the equipment management industry, machine vision has been studied more in the identification of equipment-related failures. The utilisation of natural language processing has previously been studied for example in service data analysis Stenström et al 2015, Bonada et al 2020 and supply chain management Chandler 2018 .

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Utilising Machine Learning Methods in Creating Digital Equipment Information

2. Concepts 2.1. Optical Character Recognition OCR Optical Character Recognition OCR refers to the conversion of printed or handwritten text into machine-understandable language Boesch 2021; Shperber 2018 . OCR typically involves five steps: scanning, segmentation, preprocessing, recognition of text elements, and postprocessing Mithe et al 2013 . The aim of OCR is to convert the preprocessed document into a format where the desired text elements are as easy to find as possible.

In machine-based text recognition, characters are converted to machine-readable format. More rudimentary methods look at characters as individuals and seek to determine which machine language character is closest to them, while more advanced methods can look at whole words or phrases. Mithe et al 2013 The aim of post-processing is to improve the accuracy of OCR and to use, among other things, dictionaries to correct OCR errors. The word “ddate” very likely means the date when it can be automatically corrected to the correct format.

2.2. Natural Language Processing NLP Natural Language Processing NLP refers to the use of computer science and more specifically artificial intelligence in the processing, understanding and production of human language. In addition to individual words or sentences, NLP models seek to understand larger entities and connections, so that the level of “awareness” of the processing unit about the meaning of the text can be seen as mimicking human-like reasoning. Liddy 2001 Advanced language processing models can also help to understand the context when processing documents: if an individual receipt has a protective case and adapter in addition to a computer, it is likely that they are likely to be accessories and not, for example, a mobile phone case and a TV adapter.

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Utilising Machine Learning Methods in Creating Digital Equipment Information

3. Methods 3.1. Commercial Tools Numerous ready-made tools have been developed for document processing. This study relied on a customised solution utilising open source tools such as Tesseract. At the same time, it was identified that it might be justified to rely on firmware for productional use of the built model.

3.2. Gathering Data Training data is the material used to train machine learning models. The quality and quantity of training data strongly correlated with the final performance of the model.

As the models learn from the training data, the training data must be qualitatively and quantitatively comprehensive enough. Models can always be retrained, so the performance of the models increases as the amount of data increases.

In this study, training material was collected from several organisations, and the final training material comprised a document base produced from a dozen different equipment vendor systems. About 30% of the material was PDF files and 70% of the material was in image format.

3.3. Step 1 Optical Character Recognition As a first step, the documents were processed through an OCR model, whereby, for example, the data fields of images could be imported into machine-readable text format. After the OCR phase, the textual material containing the documents was largely machine-readable in terms of textual content.

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Utilising Machine Learning Methods in Creating Digital Equipment Information

Image 1 Information fields recognised by OCR model

3.4. Step 2 Classification The second step was to classify the data. The classification of information elevates certain elements from an otherwise neutral text mass to the consciousness of the model. By classifying information, the model learns to identify the concepts sought in the training material Hankiewich 2020 .

The first step in classifying the data was to identify common and recurring elements in the documentation, such as the name of the company, the date and total amount of the purchase.

A more challenging step was to identify, in addition to the general elements, certain asset information elements that are relevant to the equipment management system. This asset information includes, for example, the device manufacturer, model, number, and purchase price.

The variability in the format of the documentation proved to be a challenge, making it challenging to find the desired data fields. As a solution, the localisation of a certain frequently repeated tabular element from the document and reasoning based on that, was tested.

3.5. Step 3 Natural Language Processing As a third step, the identification of natural language on the basis of a defined classification was utilised. The data fields thus classified could be brought to the desired categorisation and the data fields could be directed to the correct purposes in the equipment management system.

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Utilising Machine Learning Methods in Creating Digital Equipment Information

Image 2 Steps 1 3 illustrated

3.6. Step 4 Supervised Learning One of the identifiable characteristics of supervised learning is that all information is classifiable and the algorithm learns to predict the outcome based on the inputs provided. In supervised learning, the training material is used to create a function with which the data can be classified. The training material consists of inputs as well as results that should be followed from the given inputs. The goal of supervised learning is that the model is able to deduce from the training material what kind of results should be obtained with unknown inputs. Brownlee 2020 In this study, the aim was to train the model to understand the future documents and to extract the desired information from the material by understanding the previous trained documents. Supervised learning was the most appropriate method for this study because supervised learning enables training a machine-based model using several similar examples and the research material supported this method.

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Utilising Machine Learning Methods in Creating Digital Equipment Information

4. Results

As a result of the study, it can be stated that the tested machine learning methods are well suited for document processing.

The OCR model tested in the study proved to be functional and effective in the context of equipment data. The OCR model was able to convert document files to machine-readable text format with high accuracy. It can be stated that with the relatively fast implementation of the OCR method, it is possible to achieve a significant improvement in the efficiency of document processing.

The NLP model also proved to be effective in the study in classifying and processing the information contained in documents into device information. However, the reliability of the model in the study remained low due to the variation in the format of the data.

Fully automated and accurate documentation processing (with line-byline text parsing) is a complex issue, as document formats vary widely.

Teaching a model to understand a wide range of training materials is laborious but possible. With supervised learning methods, the training material should be constructed so comprehensively that it teaches practically all possible variations in the form of the material.

Alternatively, the process can be designed to involve a human in addition to the model.

The researched topic offers numerous interesting areas for further research.

Study Paper Trail Systems Ltd

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Utilising Machine Learning Methods in Creating Digital Equipment Information

5. Results Analysis

In the light of the study, the system for automating the document processing process can be developed iteratively and its accuracy can be improved over time, among other things by developing data validation methods and enriching the data with, for example, data from open data sources. It is possible to train the machine learning model more accurately over time.

From a business point of view, the collaboration between a human and a machine-learning model is identified as a more meaningful solution.

This is likely to also provide a cost-effective and productive end result.

The machine learning model for building equipment management information is interesting because it is applicable to all organisations, regardless of equipment type or industry. However, building a production-ready solution requires significant further refinement of the model.

The study focused only on the processing of data fields that were identified as potential to be found in the data set. In order to provide more complete device information, a few different methods were identified in the study that could be further investigated. Some of the methods utilise artificial intelligence and some of the methods are again relatively straightforward and can be considered to go under simple software robotics, for example. Identified methods include e.g. data scraping, a centralised database, and structured open sources.

Possible further research could also focus on identifying information that the machine learning model fails to localise. These data fields are thus inaccessible to automation and probably rely on the creation of manual information.

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Boesch G. 2021 Optical Character Recognition OCR Overview and Use Cases https://viso.ai/ computer-vision/optical-character-recognition-ocr/ 26.6.2021 Bonada F., Echeverria L., Domingo X., Anzaldi G. 2020 AI for Improving the Overall Equipment Efficiency in Manufacturing Industry, New Trends in the Use of Artificial Intelligence for the Industry 4.0, Intech Open 2020.

Brownlee J. 2020 Supervised and Unsupervised Machine Learning Algorithms, www.machinelearningmastery.com, 20.8.2020. https://machinelearningmastery.com/supervised-andunsupervised-machine-learning-algorithms/ Chandler R. 2018 NLP at Scale for Maintenance and Supply Chain Management, https://neo4j.com/blog/ nlp-at-scale-maintenance-supply-chain-management/ 24.10.2018 Hankiewich, K. 2020 Applying Natural Language Processing for intelligent document analysis, www.medium.com, 3.2.2020. https://medium.com/untrite/applying-natural-language-processing-forintelligent-document-analysis-a91bcb85919b Liddy E.D. 2001 Natural Language Processing. In Encyclopedia of Library and Information Science, 2nd Ed. NY. Marcel Decker, Inc. Mithe R., Supriya I., Divekar N. 2013 Optical Character Recognition, International Journal of Recent Technology and Engineering IJRTE 03/2013 Poor P. & Simon M., Karkova M. 2016 CMMS as an effective solution for company maintenance cost reduction, Production Management and Engineering Sciences Majemik, Daneshjo & Bosak (Eds), Taylor & Francis Group Rajapakse T. 2020 Combining Optical Character Recognition and Object Detection for Document Processing, www.medium.com, 28.1.2020. https://medium.com/skilai/combining-optical-characterrecognition-and-object-detection-for-document-processing-e8b9c488d9eb Shang S., Seddon P. B. 2000 A Comprehensive Framework for Classifying the Benefits of ERP Systems, Association for Information Systems, AMCIS 2000 Proceedings. 39. http://aisel.aisnet.org/amcis2000/39 Shperber G. 2018 A gentle introduction to OCR, www.towardsdatascience.com. https:// towardsdatascience.com/a-gentle-introduction-to-ocr-ee1469a201aa 28.10.2021 Stenström C., Aljumaili M., Parida A. 2015 Natural Language Processing of Maintenance Records Data, International journal of COMADEM, https://www.diva-portal.org/smash/get/diva2 975548/FULLTEXT01.pdf 28.10.2021

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2021

Study Paper