Unleashing the potential of Visual Document Classification
Updated: Feb 6, 2022
As a pragmatic move, implementing visual document classification seamlessly transforms your business’s entire document management framework. Working with exceptional computational accuracy, it helps easily overcome traditional challenges and enables smooth data retrieval and storage.
The fast emergence of diverse, non-traditional data sources, and evolving data formats have been rendering a complex character to documents. Indeed, today’s documents don’t carry only text, but complex visuals and graphics, handwritten texts to scanned objects.
Incidentally, around 67% of organizations struggle to access relevant documents on time due to a lack of good document classification processes. Thus relying solely on human efforts, traditional document classification fails against changing dynamics.
The prime factors that dictate today’s documentation dynamicity are first data retrieval, summarization, and organization. Secondly, the importance of a given document, processing time, and memory consumed also play an equally important role. Altogether, these factors demand an extraordinary classification model.
As a combination of potentially viable machine learning and deep learning techniques, visual document classification works as a mechanism to correctly categorize documents into functional groups. It doesn’t rely on a single text analytics technique, as is not sufficient for successful classification, but brings together all mechanisms that best suit and work for a given problem.
Let’s know more about visual document classification, and how it can practically benefit businesses operating across domains.
What is Visual Document Classification?
To interpret as per Wikipedia’s definition, document classification works as a class of classification approaches, comprising intellectual or algorithmic classification.
In the industrial and business context, visual document classification works as an algorithmic classification mechanism that categorizes documents. This classification works as a system that analyzes documents based on image and visual input and consolidates both paper and electronic silos of information.
Typical visual document classification software leverages a set of technologies like computer vision and natural language processing-based text analytics to help sort the data with utmost perfection. So, based on the nature of the data, visual document classification applies image classification or text classification to extract and infer patterns from the source data.
How Visual Document Classification works
On a high level, visual document classification analyzes documents using artificial intelligence (AI) and classifies them into relevant categories. It works as a dynamic process, because depending upon the text and layout complexities, documents assume a structured, semi-structured, or unstructured nature.
While structured documents depict consistent formats and styles, unstructured documents are exactly the opposite of that, with no particular order. As against structured documents, in unstructured documents, the data values are not directly extractable, which poses critical classification challenges. Interestingly, today, around 80 percent of information comes from semi-structured and unstructured documents.
Visual document classification, thus, is highly contextual and needs to be fit into a suitable machine learning technique. Ideally, a visual document classification cycle starts with the scanning of documents using optical character recognition (OCR). Converted into unique formats, these documents are fed into a Machine Learning Engine, which performs the core task of document categorization. Finally, the documents are routed to respective repositories, from where stakeholders can access them.
Why Visual Document Classification is important for enterprises
The indispensable need to classify documents is compelling enterprises to discover concrete visual document classification solutions. Following are a few reasons explaining the functional importance of visual document classification.
Rightly classified documents increase data accuracy i.e. reduces the error rate. Streamlining operational processes, this saves several hours that can be devoted to core strategic business activities.
Automatic document classification helps build a centralized database and provides the ability to deal with sensitive information, thereby reducing data risks.
Improving speed of execution and ease of access, classification allows smooth interactions amongst stakeholders and prevents hassles, otherwise prevalent in paper-based transactions.
Operationally, having ready-made categories of documents allows easy deliverability of information to customers, given that different data gets captured in different documents.
When your documents make inputs to analytics systems, the analytics sourcing mechanism has a clearly defined data sourcing pipeline.
The remarkable agility that the entire document processing and management framework achieve through the right classification comes in handy in fast operational expansion, without the requirement of additional resources.
Applications of Visual Document Classification
As a generic process that can be contextually configured, visual document classification can be successfully applied across industries and verticals. Discussed below are applications of the concept in a few domains.
Supply Chain and Logistics
Supply chain processes are document-intensive and despite applying effort for building a paperless framework, companies struggle. One of ODA’s clients, a leading supply chain and logistics company from the US, was grappling with an acute invoice classification problem. Besides categorizing documents with 95% accuracy, our deep learning model expedited the payment process, thus fostering strong relations with vendors.
Document management is an overwhelming task for healthcare providers. Every single day, they have to handle several thousand documents, right from invoices, physician prescriptions, balance sheets, accounting documents, pharmaceutical documentation to health reports. Classifying documents boost the efficiency of entire healthcare by helping physicians, nurses, and admin staff to tap in needed information quickly.
The real estate sector has undergone rapid changes in recent years, especially with the emergence of multi-listing sites. Swamped with documents, realtors struggle to classify them manually. Visual document classification in real estate helps to easily separate and classify mortgages, titles, sale deeds, invoices, taxes, and all other documents that form a part of a property transaction.
Banking and Finance
Documentation in the banking and financial sector is linked to regulations, user identity, and fraud management. Increasing the volume of banking transactions simply leaves manual document management infeasible. A machine learning-based solution not just classifies documents, but implements data validation and data verification as a precursor for authentication, before categorizing documents, thereby assuring bankers with a robust data security mechanism.
Implement an intelligent Visual Document Classification solution for your business
Outdated and ineffective, traditional document classification severely hampers your operational productivity. On the other hand, adopting an intelligent machine learning-based solution helps implement a flawless document classification mechanism that significantly optimizes time. And since time relates to dollars, why not consider a smart visual document classifier for your operational processes?
Productivity-oriented, Optimum Data Analytics’s visual document classification solutions offer businesses economies of scale. Our flexible models integrate easily with your documentation setup and allow you to configure classification levels as per your requirements. As consultants, we use our expertise to enhance your document classification processes so that they produce accurate results.