10 Issues Machine Learning OCR Can Solve for Your Business

Errors in document processing are a pain point for many businesses. Thankfully, technological advances are now able to help with many of them. Here are some common issues machine learning and OCR can solve.

June 23, 2023
6 minutes
Explore what ML and OCR can do for you
Explore what ML and OCR can do for you

Table of Contents

Are you struggling with manual data entry, processing bottlenecks, or the risk of compliance breaches in your business?  

Traditional document processing methods often fall short when it comes to handling the increasing volume and complexity of both physical and digital paperwork.

By pairing the power of machine learning algorithms with OCR software, you can efficiently process large volumes of documents, significantly reduce errors, and improve data validation.

However, let’s start with the 10 common issues that this technology can solve for your business, and then explain how it all works.

10 Problems ML-Powered OCR Software Solves

1. Poor Efficiency and Unnecessary Manual Labour

Traditional document processing methods often involve time-consuming manual data entry that impacts the efficiency of knowledge workers.  

In fact, 42% of workers shared that paper-based workflows not only make their days less efficient but are also expensive as workers spend anywhere from 21 - 30% of their week on these tasks.  

However, an ML-based OCR tool can automate data extraction by analysing and understanding the content of documents, whether they’re invoices, legal paperwork, or any other type of document.  

It can also process large volumes of documents in a fraction of the time it would take a human, freeing up your staff for more important tasks.

2. Human Errors

Manual data entry is susceptible to human errors such as typos, misinterpretation, and data inconsistencies.  

These errors can have significant consequences for businesses as they lead to inaccuracies, delays, and potential financial losses. For example, mistyped characters or data inconsistencies can create a ripple effect throughout the entire invoice processing workflow as managers base business decisions on faulty data.  

Machine learning OCR provides a powerful solution to mitigate these errors by accurately extracting information from documents using advanced algorithms.  

This ensures data accuracy and consistency, leading to better decision-making and reduced operational risks.

3. Inconsistent Formatting

Inconsistent formatting is a common challenge when it comes to document processing.  

Since documents come in various formats, layouts, and structures, it’s challenging for basic OCR systems to extract information consistently.  

However, machine learning OCR systems are trained on large datasets, which enables them to handle a wide range of document types, fonts, and languages. Additionally, through the use of advanced techniques like pattern recognition and natural language processing, these systems can adapt to different formatting variations.

As the tool encounters more documents with diverse formatting, its algorithms learn to recognize and interpret the information consistently.  

This continuous learning process enhances the OCR system's ability to handle different formatting styles, resulting in more reliable and accurate data extraction.

4. Processing Unstructured Text

Analysing unstructured text is a common hurdle in data processing, as it often includes handwritten documents, free-form text, or documents with irregular formatting.  

However, machine learning OCR offers a powerful solution to this challenge thanks to its ability to employ advanced techniques, such as natural language processing (NLP).

With the application of NLP, machine learning OCR can go beyond basic character recognition and understand the context and meaning behind the text. It can identify key entities, extract relevant information, and even classify the content of unstructured text.  

This allows you to extract valuable insights and derive meaningful data from handwritten documents or free-form text that were previously difficult to process.

5. Physical Storage Costs

Physical storage costs can be a significant burden for businesses as maintaining physical documents requires dedicated filing cabinets and resources for organisation and storage.  

You can easily and accurately digitise your documents by employing machine learning.

Digitizing documents not only eliminates the need for physical storage space but also streamlines document management processes. With digital documents, you can easily organize, search, and retrieve information with just a few clicks.

This solves the pressing issue of 49% of workers being unable to easily locate documents.

Instead of manually sifting through paper files, you can access the necessary documents digitally, enhancing productivity and efficiency.

6. Weak Security

Security is a critical concern for businesses, especially when it comes to sensitive and confidential information contained in documents. Still, physical documents are inherently vulnerable to risks such as loss, theft, or damage.

Instead of relying on physical storage methods, you can encrypt and store your processed documents in secure databases or cloud-based platforms.  

These digital storage systems provide multiple layers of security, including access controls, authentication mechanisms, and encryption protocols. This ensures that only authorized personnel can access the documents.

Furthermore, some machine learning OCR systems include built-in security features that safeguard sensitive information during the extraction and processing stages.  

These features can include data anonymization techniques, redaction capabilities, and audit trails to track document access and modifications.  

7. Language Compatibility

Traditional OCR systems often struggle with documents in languages other than the ones they were primarily designed for.  

However, machine learning OCR leverages advanced techniques such as natural language processing and deep learning algorithms to overcome these barriers.

Since you can train these systems on large datasets across various languages, they can support multi-language recognition.  

Whether it's contracts, invoices, or legal documents, machine learning OCR can effectively extract multi-language information from these documents, making it a powerful tool for global businesses.  

8. Manual Data Validation

Verifying the accuracy and validity of manually entered data can be time-consuming and error-prone, leading to potential data inconsistencies.

With machine learning OCR, you can enable automated data validation during the document processing workflow.

Once the information is extracted from documents, the OCR system can cross-reference the extracted data against predefined rules and databases. These rules can include data formats, specific patterns, or validation criteria that ensure the accuracy and integrity of the extracted information.

Since automated validation significantly reduces the need for manual intervention and verification, you save valuable time and resources that would otherwise be spent on these tasks.

However, there are use cases when the human-in-the-loop is a good thing. Advanced OCR based invoice processing solutions, for example, have this feature in their data validation process, so people can be involved in data validation as much as it’s necessary or required by the company.

9. Processing Bottlenecks

In today's fast-paced business environment, the sheer volume of documents that you have to process can be overwhelming.  

For most organisations, manual processing workflows are often unable to keep up, resulting in processing bottlenecks and delays.

Machine learning OCR systems are designed to handle large-scale document processing with speed and accuracy. Through the power of advanced algorithms and computing, these systems can quickly extract information from a vast number of documents.

Unlike manual processing, machine learning OCR can do batch document processing, allowing for faster turnaround and better efficiency.

10. Compliance Breaches

Compliance breaches can have severe consequences for businesses, including legal penalties, reputational damage, and loss of customer trust.  

One of the key ways machine learning OCR addresses compliance breaches is through accurate and reliable data extraction which significantly reduces the risk of human error and ensures the integrity of the data.  

This is particularly crucial when dealing with sensitive information such as financial records, personal data, or confidential documents.

Since you can train these OCR systems to recognize and classify different types of documents based on their content and context, you can also implement automated compliance checks. These ensure that all the necessary information is present and meets the required standards.  

For example, in the context of financial compliance, machine learning OCR can identify and flag any discrepancies or missing data in invoices, receipts, or financial statements. And it can do this with an accuracy rate of 99%.

Furthermore, machine learning OCR supports the implementation of compliance workflows and audit trails.  

By capturing and digitizing documents, businesses can establish a comprehensive record of their compliance activities. This includes tracking the processing, storage, and access of documents, and maintaining an audit trail of any changes made to the data.

After all this talk about OCR and machine learning, it is time to pop the hood open and look at this business-saving engine a bit closer.  

What Is Optical Character Recognition (OCR)?

Optical Character Recognition (OCR) is a technology that enables you to convert physical or digital documents into machine-readable text.

At its core, OCR utilizes optical scanners or specialized circuit boards to copy or read text from physical documents.  

Extraction using OCR begins with the scanning of physical documents which involves converting the document into a two-colour or black-and-white version.  

The OCR software then uses advanced processing to analyse the text’s contrasting colours to identify letters, numbers, and other characters. It then organizes these characters into words and sentences enabling easy access to the original content without the need for manual data entry.

A common OCR real-world use case is the conversion of hard-copy documents into PDF files.  

By transforming physical documents into digital formats, OCR empowers users to edit, format, and search the content as if it were created with a word processor. This not only streamlines document accessibility and manipulation but also facilitates efficient document retrieval and collaboration.

What Is Machine Learning?

Machine learning is a branch of artificial intelligence (AI) and computer science. It focuses on utilizing data and algorithms to simulate the way humans learn and improve over time.  

At ML’s core are algorithms that you can train using statistical methods that enable the algorithm to make classifications or predictions. These algorithms are often developed using frameworks, such as TensorFlow and PyTorch, to enable efficient implementation.

There are a few ways to approach machine learning depending on your needs:

  • Supervised Learning: This approach uses labelled datasets to train algorithms. The model dynamically adjusts its weights as input data is fed into it to ensure greater accuracy. However, cross-validation should be employed to prevent over- and under-optimisation.

  • Unsupervised Learning: Taking an opposing approach, unsupervised learning uses unlabelled datasets to train algorithms. These algorithms aim to identify patterns within the data without specific predefined labels. Unsupervised learning is particularly useful for tasks such as clustering or anomaly detection.

  • Semi-Supervised Learning: Semi-supervised learning is a hybrid of the first two approaches. It employs a smaller labelled dataset to guide classification and feature extraction from a larger, unlabelled dataset. This approach helps address the challenge of having insufficient labelled data for a supervised learning algorithm and can also mitigate the cost associated with labelling a large volume of data.

How Does ML-based OCR Processing Work?

Like basic OCR processing, ML-based OCR processing aims to convert text from a digital image into machine-readable text.  

But when it comes to information extraction using OCR and machine learning, the process goes beyond simple character recognition. It involves extracting relationships, structure, and text positioning from documents, significantly broadening OCR’s capabilities, and performance.  

At the heart of ML-based OCR technology is a Convolutional Neural Network (CNN) model that leverages embedded text recognition to extract and process text from images.

Without getting too far into the technical side of things, here’s an overview of how it works:

1. Image Pre-Processing  

The tool begins by converting an image-based document, such as a PDF, into a JPEG or another supported file type to improve its resolution. Sometimes, images are also augmented or magnified to enhance clarity and readability.

2. Content Extraction

The extraction process occurs in two phases. In the first phase, relevant regions on the document are identified and marked with a bounding box to create separation.  

For example, if you were processing an invoice, the tool would create bounding boxes around data like the payment total, contact information, invoice number, etc. Then the tool employs OCR to recognize the text within each region.  

It then passes the scanned words as input to the machine learning layer, which utilizes neural networks to accurately "read" and classify the text.  

So instead of the data (such as supplier contact information) having to be in the same location across different documents, the tool can accurately identify and label it regardless of any variation.

3. Output Generation

Once the tool has identified all the document’s data field names and corresponding values, it can present them in a tabular format of your choice.

Advanced tools offer the flexibility to export the extracted data into standard formats such as Excel, CSV, and JSON. Some tools even provide APIs that allow you to automatically push the output into your database of choice, eliminating the need for manual intervention.

How to Start Implementing Machine-Learning OCR Into Your Business?

In our digital age, you need efficient and reliable solutions to tackle the challenges of document processing.  

Machine learning OCR offers comprehensive capabilities that can revolutionize the way you handle documents in your business. From automating data extraction and validation to enhancing security and ensuring compliance, machine learning OCR empowers you to overcome the limitations of its predecessors.

Want to experience the power of machine learning OCR firsthand?  

Then claim your Affinda free trial, and utilise the latest advancements in AI technology to optimize your document processing workflows.

With its advanced algorithms and intuitive interface, Affinda simplifies the extraction and organization of your documents, improving productivity, and accuracy.

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