September 21, 2022

Document Classification and Data Extraction using LayoutLM

Data extraction and classification is one of the most primarily function while document processing. In this article, we have described how LayoutLM model helps with the task.

Introduction

Rapid advancements in AI technologies have enabled almost all businesses to evolve quickly, which will be more so in the future. Organizations these days can handle massive volumes of data using the tools and technologies of AI. Data Extraction is one task simplified by AI and has expedited the workflow in organizations implementing automation. 

It is easy to see that any organization can make better judgments with access to more information. Therefore, many organizations rely on several methods of acquiring, categorizing, and storing big data for further processing needs. On the other hand, humans are incapable of efficiently managing big data or vast numbers of documents at once. This is where Automatic Document Classification steps in. It not only helps businesses to save the information but also allows searching for them when needed.

Importance of Document Classification 

Documents in an organization are a crucial source of information, regardless of industry. This data assists companies in gaining insights into their performance. These documents containing data generally comprise both text and visuals. It is easier for the tools to process digital documents than scanned materials.

An AI model needs the data to be machine-readable to analyze data. Earlier, humans manually processed documents to collect this data and made it machine-readable, but with newer AI technologies, all this has now started to change. For example, accurately processing and storing several invoices like reimbursements, office expenses, employee expenses, purchase orders, etc. received regularly, becomes time-consuming and tiresome if done manually. Hence, companies usually prefer automated AI solutions for quickly and reliably classifying documents.

What is Intelligent Document Processing (IDP) ?

Intelligent Document Processing (IDP) is the latest data entry and processing technique that comprises extracting, classifying and mapping data from documents. It achieves this by leveraging Optical Character Recognition (OCR), Natural Language Processing (NLP), Computer Vision, and Machine Learning before passing it downstream to AI-powered analytics tools or human analysts for insights. Since it uses AI, IDP is effective, quick, and reliable. It can handle digitized and scanned documents with images and text, expediting document processing workflows and improving overall organizational efficiency for document processing tasks.

What is LayoutLM?

These days, IDPs are implemented for more than just collecting text or numbers from structured documents such as cheques. Computers can now gather, identify, and label data from various documents and put it into a data analytics tool such as Excel, Power BI, or Tableau for dashboards and further analysis.

As IDPs such as VisionERA eliminate the need for human staff to handle repetitive documents, it is a time and money-saving tool. Furthermore, it enables businesses to obtain critical insights from data accurately and quickly, which is why IDP is used in finance, healthcare, education, travel, and several other industries for document processing. Although multiple IDP models are currently available, LayoutLM is one of the most effective pre-trained models. LayoutLM model excels in document processing tasks such as understanding forms, receipts, and document-image classification. It is the first IDP platform that used text and layout information in context with images to improve document image and text interpretation. Hence, it is a cutting-edge technique for processing images and documents. LayoutLM’s V1, V2, and V3 are subsequent improvements over the previous ones because they have been trained on larger datasets and use novel methods for image extraction and layout understanding. 

Applications of LayoutLM and similar IDPs can be document processing tasks such as Automating Invoice Processing, Table Data and Form Data Extraction, parsing cheques, contracts or resumes, and so on.

LayoutLM Pre-trained Text and Image Model

Inspired by the BERT model, the LayoutLM model (2019) was proposed by authors Yiheng Xu, Minghao Li, et al. in the paper "LayoutLM: Pre-training of Text and Layout for Document Image Understanding." The open-sourced LayoutLM model available on Hugging Face uses the Tesseract open-source library for text extraction. The model can be fine-tuned for custom document processing requirements. To understand the model structure and to work, it is essential to have some prior knowledge about 'Transformers.'

Here is how LayoutLM works - 

  • The first step in the document processing task is recognizing the text and identifying its location using OCR technology. 
  • Before labeling or classification in LayoutLM, the OCR engine identifies the text and determines its location on a document with the help of bounding boxes. For determining the location on a document, location (0,0) or starting point is always at the top left corner, the x-axis runs horizontally and the y-axis runs vertically from this point.
  • The recognized coordinates are then passed through embedding layers to codify them for the model. For every text piece on the invoice, the final embedding consists of the text and position embeddings and is then passed on to LayoutLM. In other words, the input for LayoutLM is the OCR-extracted locational and character information.
  • The next step would be Image Embedding. LayoutLM requires the image location and interpretation as input, i.e., if there are images or pieces of text in the document that cannot be identified as characters. For this, an image model like Faster R-CNN is better suited to perform object detection. In this step, the text, location, and image embeddings gathered from OCR and Faster R-CNN are combined to form the input for LayoutLM downstream tasks such as form and receipt understanding and document classification.
  • The LayoutLM has been trained on the IIT-CDIP test collection containing millions of scanned documents and scanned document images. With this pre-training, LayoutLM performs well for recognizing and processing invoices. However, it may require some additional training to accurately and reliably process different invoice formats. 

Before training or fine-tuning LayoutLM for custom use, it is essential to remember that to properly label the data, LayoutLM needs each word and its correct location. Additionally, it is critical to define labels for each word while fine-tuning LayoutLM which allows the model to perform sequence labeling better and accurately categorize each word.

Conclusion

Manual processes for document classification are not only time-consuming but are also prone to mistakes. LayoutLM and its subsequent versions, as well as any Intelligent Document Processing (IDP) tool, are a good solution for document processing needs. Especially, IDPs enable businesses to automate the document processing tasks such as extracting, cleaning, and organizing scattered data across many documents quickly, economically, and reliably. It is currently the best method for document processing available on the market when used in conjunction with humans-in-the-loop for edge use cases. LayoutLM is perfect for businesses with smaller labeled datasets available for training. They can simply fine-tune the LayoutLM model to quickly achieve good document processing results.

Although LayoutLM can address the document classification needs of any business, companies usually prefer implementing an end-to-end solution such as an IDP. It is because an IDP does not require coding expertise, offers document processing, and extracts relevant insights from the processed data from the documents. 

About us: VisionERA is an Intelligent Document Processing (IDP) platform capable of handling various types of documents for text recognition and document classification. It has the capacity to extract and validate data for bulk volumes with minimal intervention. Also, the platform can be molded as per requirements for any industry and use case because of its custom DIY workflow feature. It is a scalable and flexible platform providing end-to-end document automation for any organization.

Are you looking for a document processing solution that uses the enhanced capabilities for text recognition in deep learning? Setup a demo today by clicking the CTA below or simply send us a query through the contact us page!

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