In a world driven by technological advancements, many tasks and roles within a business still involve physical documents such as invoices, contracts, recruitment documents, legal documents and more. If you are someone who regularly works with such documents, you'd know all too well that it takes a lot of time to read and process the information and doing it the manual way often leads to errors.
Luckily, you don't have to do your document processing the hard way anymore. One of the latest tech innovations, machine learning OCR, has made it much easier to accurately and efficiently automate document processing tasks.
Understanding OCR and machine learning
What is OCR?
OCR is a type of technology that allows you to extract information from a document and turn it into searchable and editable data. Whether it's a physical paper, a scanned document, or a non-searchable PDF, OCR digitises the data. Once extracted, this information can be integrated into systems like Enterprise Resource Planning (ERP) or content management platforms for further processing, such as invoice handling, HR and legal document searchability and sales order processing.
When we add in elements of AI and machine learning for OCR, we can further minimise the amount of human intervention that is needed and recognise a broader amount of document types and languages – or even mimic the way the human brain recognises patterns and context.
What is machine learning?
Machine learning is a subset of artificial intelligence (AI) that uses self-learning algorithms to derive meaningful knowledge and insights from data. It starts with 'training' which involves using data to create and fine-tune a predictive model. This model can then make predictions on new, unseen data to answer various questions. The more data that becomes available to it, the better the model improves over time. over time.
There are three types of machine learning:
- Supervised learning where the computer is trained on labelled data
- Unsupervised learning which involves finding patterns in unlabelled data and,
- Reinforcement learning which teaches a computer to perform tasks through trial and error.
Machine learning is all about teaching a computer to perform tasks by learning from data.
How does OCR and machine learning work?
When paired together, the capabilities of OCR and machine learning extend far beyond simple character recognition.
Using a Convolutional Neural Network (CNN) model, OCR can now extract and process text from images, identifying relationships, structure, and text positioning in documents, much like a human would.
It works like this:
- Data Pre-Processing
The tool begins by converting an image-based document (e.g. PDF to JPEG) to improve resolution. Preprocessing techniques such as resizing, normalisation, and noise reduction are applied to enhance image clarity and readability. This step may involve de-skewing images, removing spots, and smoothing text edges to prepare the data for accurate text extraction.
- Content Extraction
This occurs in three phases:
- Relevant regions on the document are identified and marked with bounding boxes to separate them from other elements on the page. For example, when processing an invoice, bounding boxes are created around data like payment total, contact information, and invoice number. OCR then recognises the text within these regions.
- The scanned words within these regions are passed to a machine learning layer, which uses neural networks to read and classify the text. This means the tool can find and label data, such as supplier contact information, regardless of its location on different documents.
- Output Generation: after identifying all data fields and values, the tool presents them in a tabular format. Advanced tools can export this data into formats like CSV, Excel and JSON. Some even offer APIs to automatically push the output into a database, removing the need for manual intervention.
5 ways machine learning OCR software can help your business
In the realm of modern business, machine learning for OCR is transforming how operations are conducted effectively and efficiently while ensuring accuracy and compliance across all business functions. Here’s how:
- Automating data entry & reducing errors
Traditional document processing involves time-consuming data entry, which not only reduces employee efficiency but is also prone to human errors. Mistakes like typos and inconsistencies can lead to inaccurate data input causing delays and potentially leading to financial losses for your business.
For example, mistyped expenses can disrupt the entire invoice processing workflow, which results in managers making decisions based on faulty data. Similarly, inaccurate extraction from legal documents can result in non-compliance, incurring fines or legal penalties.
According to the McKinsey Technology Trends Outlook 2024, there is a continued focus on investing in automation technologies, which are expected to drive significant improvements in operational efficiency and productivity. So, while manual workflows still exist, the growing adoption of automation is gradually reducing their negative impact on staff productivity.
Quality OCR and machine learning software can automate data extraction by analysing and understanding any document type—whether invoices, ID documents, or legal contracts and more. Using advanced algorithms, the tool will accurately extract information from a large volume of documents, leading to better decision-making and reduced operational risks.
- Processing complex documents
Inconsistent formatting and unstructured data are common challenges in document processing. Invoices from various suppliers, with differing templates, fonts, varying tables and languages or handwritten notes are just some examples of how complex a document can be.
OCR and machine learning models are trained on large datasets, leveraging pattern recognition and natural language processing so they can manage diverse document types. As they encounter more variations, these systems continuously improve their accuracy, successfully recognising and interpreting information.
By understanding the context and meaning of data, machine learning OCR identifies key entities, extracts relevant information and classifies content, providing valuable insights from complex documents allowing you to derive valuable insights from those difficult-to-process case study papers that your boss asked you to enter in the system.
- Reducing operational costs
Traditional document management systems require manual data entry and physical storage, which is inefficient and expensive. Companies who are using intelligent automation systems have reported an average cost savings of 32%. So, it’s no wonder businesses are increasingly turning to AI as a strategic tool amid economic uncertainties.
Gartner’s recent survey revealed that 61% of businesses are planning to boost their technology budgets in 2024, with 92% aiming to adopt AI-powered solutions. This trend highlights the growing recognition of AI's potential to streamline processes and drive productivity.
Employing a machine learning-based OCR document management system automates data entry and enhances document organisation, enabling easy searching and retrieval while eliminating the need for physical storage.
Investing in an AI document automation software not only reduces immediate expenses, but it also frees up your employees to focus on more valuable tasks positioning your business for long-term strategic growth.
- Strengthening security & mitigating compliance risks
How confident are you in your organisation’s ability to protect confidential information and meet regulatory requirements when it comes to storing your documents? Is your storage system safe, or could your business be at risk of security breaches and compliance violations?
Encrypting and storing documents in secure databases or cloud-based platforms eliminates risks associated with physical storage, such as loss, theft, or damage. It provides a security framework that includes access controls, authentication mechanisms and encryption protocols to ensure that only authorised personnel can access sensitive information. The high level of security achieved helps mitigate compliance risks.
Compliance breaches can result in severe legal and reputational consequences for your business. Digitising your document management process supports compliance workflows and audit trails, so you can track processing, storage, access and changes. Security features like data anonymisation, redaction capabilities and audit trails safeguards sensitive data and supports compliance by keeping detailed records of document access and modifications.
Machine learning OCR systems play an important role in strengthening security and mitigating compliance risks in document management. A quality system can be trained to recognise and classify document types, facilitating automated compliance checks to ensure information meets required standards.
Affinda's AI document processing solution can be customised to handle documents containing personally identifiable information (PII) by automatically detecting and classifying them. It provides a risk score and explanation for each document type allowing businesses to set up proper security measures for high-risk documents, reducing risk exposure and ensuring compliance.
- Streamlining business processes
The volume of documents that require processing in today's fast-paced business environment can be overwhelming. Checking the data from these documents by hand takes a lot of time and can easily lead to mistakes, which might cause the data to be inconsistent. Most businesses find that manual workflow systems often cannot keep pace, leading to bottlenecks and delays in processing documents.
Imagine being an accountant receiving an average of 80 invoices each week that need processing. With manual workflow systems in use, this would require you to spend significant amounts of time entering data manually into systems before trying to interpret what has been entered, increasing the risk of error or oversight.
Features of a machine learning OCR software such as batch document processing enable efficient handling of multiple documents simultaneously so you can say goodbye to spending hours processing invoices (or any other type of document) by hand. It quickly and accurately extracts complex information from complex documents and layouts into digital, searchable formats, allowing for automated validation within your document processing workflows.
OCR systems can cross-reference extracted data against predetermined rules and databases, ensuring accuracy and integrity. This automated validation reduces the need for manual intervention, saving time and resources.
While OCR solutions greatly improve efficiency, human involvement remains essential for data validation. Automation paired with human oversight enhances accuracy in workflows like invoice processing and customer verification.
Implementing machine learning OCR into your business
Now that you understand how machine learning for OCR works and the ways that AI document automation software can help your business, let's talk about how to act.
1. Select the right OCR solution
When implementing machine learning OCR in your organisation, it is important to select a solution that meets your needs and supports your business goals. Here are a few things to consider:
Start by clearly outlining your document processing requirements:
- What types of documents will you be managing?
- What volume of documents do you need to process?
- What level of accuracy is necessary for your operations?
As your business grows, your machine learning OCR solution should grow with you:
- Look for scalability and advanced features, particularly the ability to customise the solution to your needs
- Choose technology with deep learning capabilities to manage complex documents
- Prioritise user-friendly interfaces for easy adoption across your team
Consider the full financial picture:
- Account for all costs, including setup, licensing, maintenance and IT support
- Weigh up costs against the potential benefits of automated document processing
Make an informed decision by:
- Reading user reviews on reputable tech blogs (here's Affinda's G2 page, for example)
- Assessing the quality of vendor support
- Choosing a forward-thinking partner committed to ongoing AI improvements
2. Integrate with existing systems
When selecting a document processing solution, ensure it aligns seamlessly with your current IT infrastructure including data management systems, databases and operational software tools is key to a successful implementation.
Prioritise AI document automation software that offer strong APIs and integration capabilities to streamline data flow, while ensuring the system has strong security features and complies with relevant data protection regulations.
Lastly, opt for a system that offers customisation options to address any specific business requirements that you are trying to address and ensure it is a customisable and scalable solution.
Ready to get started with OCR machine learning?
In a world where technological advancements are the norm, staying competitive means employing the best technologies that can transform your operations from traditional, manual processes to streamlined, automated workflows. By employing machine learning OCR for your document processing, you will not only keep pace with the digital landscape, but also position your business for sustainable growth and success.
Book a demo with Affinda today to start your automation journey with our advanced document AI platform. Capable of processing any document type, Affinda offers a solution for every business.
