Optical character recognition (OCR) data extraction has become essential to managing, organizing, and processing business data.

This process helps turn unstructured content into structured, business-ready data, unlocking valuable insights and streamlining workflows.

Industries such as finance, healthcare, and legal services greatly benefit from OCR technology, as it automates the extraction of vital information from various paper documents, including articles, reports, receipts, forms, and invoices.

As a result, OCR data extraction has become a cornerstone of modern data management, supporting businesses in their quest for effective document processing and digital transformation in finance.

What is optical character recognition (OCR)?

First, look at what OCR is and what tools are needed for data extraction.

What is OCR data extraction?

Optical Character Recognition is a technology that converts typed, handwritten, or printed text from images into machine-encoded text using computer vision techniques.

This process enables computers to read and understand text from various sources, such as scanned documents, photographs, and digital images.

This method can transform unstructured data, like text within images, into a structured format that humans can quickly process and analyze.

OCR Data Extraction is commonly used to minimize data entry for document management.

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OCR software and scanners

To perform OCR Data Extraction, specialized software and scanners are used to extract data.

Optical character recognition software typically has algorithms and machine and deep learning capabilities to recognize different fonts, handwriting styles, and languages.

OCR scanners are devices designed to capture images of documents or textual data and convert them into digital data and files.

The combination of optical character recognition software and scanners enables the conversion of unstructured data into a structured format, such as capturing images from a scanned document into searchable and editable text.

How does OCR data extraction work?

Optical Character Recognition data extraction systems use AI to read and process content from various images and documents.

These various document formats include PDFs, scanned images, and digital files.

OCR also recognizes different styles, sizes, and text orientations, minimizing the need for manual data entry.

By analyzing the visual data, OCR can identify and extract textual content.

What are some OCR data extraction techniques?

There are various techniques for performing data extraction.

1. Structured and unstructured data extraction

OCR data extraction involves analyzing scanned documents to extract structured and unstructured data.

Structured data extraction involves identifying specific fields or patterns like dates, addresses, and phone numbers.

On the other hand, unstructured data extraction deals with extracting more complex information, such as relationships between different entities, from text data.

The accuracy of OCR data extraction depends on several factors, including the quality of the scanned documents and the capabilities of the OCR software.

2. Key-value pairs extraction

One standard OCR data extraction method is key-value pairs extraction.

This technique involves identifying specific data fields within a document and their corresponding values.

For example, key-value pairs might include the invoice number, date, and total amount in a scanned invoice.

By identifying these pairs, data entry tasks are significantly streamlined, reducing the time-consuming need for manual input and thus minimizing the likelihood of human error.

3. Table extraction and box and line detection

Another important OCR data extraction technique is table extraction, which requires identifying tables within a scanned document and extracting their data.

Often, this involves box and line detection, the process of identifying lines and boxes in the record to determine the table’s structure.

After detecting the table’s construction, OCR software can accurately extract text information from individual data cells.

4. Using neural networks for OCR data extraction

Neural networks have become increasingly popular in OCR data extraction.

These robust artificial intelligence-based systems can analyze complex documents with varying fonts, layouts, and handwriting styles.

Using neural networks, OCR software can provide greater accuracy in data extraction tasks, recognizing printed letters and handwriting.

Ultimately, OCR data extraction techniques aim to streamline data entry processes, minimize the risk of errors, and improve overall efficiency.

5. Natural language processing in OCR

Natural Language Processing (NLP) is crucial in OCR data extraction.

NLP techniques help analyze digital documents, understand the content, and extract information from them.

One of the main benefits of incorporating NLP in OCR systems is the ability to make scanned images and printed text searchable, which helps in effective data capture and retrieval.

NLP algorithms work by analyzing the patterns in the text data, identifying the structure of sentences, and understanding the relationships between words.

This enables OCR systems to recognize and extract meaningful information from the unstructured text in scanned documents, thus improving the extraction process’s accuracy and efficiency.

6. Intelligent document processing

Intelligent Document Processing (IDP) is a more advanced application of OCR technology.

It combines OCR capabilities with machine learning and artificial intelligence to extract valuable insights from digital documents.

IDP solutions can automatically identify document types and their content, accurately extract structured and semi-structured data, and classify that information into relevant categories.

The main advantage of using IDP is handling complex documents with varying formats and structures.

It enables organizations to process and analyze large volumes of digital documents, leading to better decision-making and increased operational efficiency.

Some common use cases of IDP include invoice processing, form and data analysis, and contract review.

7. Implementation of machine learning and deep learning

Machine Learning models (ML) and Deep Learning (DL) techniques are increasingly integrated into OCR systems to enhance data extraction capabilities.

By implementing ML algorithms, OCR technology can learn from the data and improve its performance over time.

This allows the OCR system to adapt to new formats and layouts, thus increasing its accuracy and reliability.

Deep Learning, a subset of Machine Learning, can be beneficial in OCR applications where there’s a need to recognize and extract information from images or handwritten text.

For example, convolutional Neural Networks (CNNs) have proven highly effective in detecting image text features and patterns.

Incorporating Deep Learning models into OCR systems can significantly improve the ability to process complex documents or extract data from low-quality images.

What role does OCR data extraction play in Shoeboxed’s app?

Shoeboxed is a popular receipt-scanning app that focuses on helping individuals and businesses organize and manage their documents efficiently using OCR data extraction software.

Shoeboxed's official homepage

Shoeboxed is a receipt scanner that uses OCR data extraction.

Not only does Shoeboxed scan receipts, but they go above and beyond what other receipt scanning apps offer by scanning any document.

Receipt scanning app

As a document scanning or receipt scanning app, users scan their information using the mobile app on their iOS or Android phones.

All receipts on Shoeboxed!

Shoeboxed’s mobile app for scanning receipts and documents can be used on the go

The data is OCR-extracted by capturing and reading the text within the image.

The line items from the image files are then converted into digital data.

The data is human-verified and uploaded into a Shoeboxed account, where the printed or handwritten text is converted into searchable data.

For receipts, Shoeboxed offers a document classification feature, categorizing the receipts into different tax categories.

With this information, users can fill in digital filing systems and accounting software fields or easily search for receipts in case of an audit.

Receipt scanning service

Another option is stuffing receipts or documents into a pre-paid envelope provided by Shoeboxed called the Magic Envelope and mailing them to Shoeboxed, where they will scan the information using OCR and automate data extraction into a Shoeboxed account.


Documents and receipts can be mailed to Shoeboxed in a Magic Envelope

Shoeboxed’s OCR technology is accurate and reliable, making it an excellent choice for those who want to automate their data extraction process.

Shoeboxed Demo by Shoeboxed YouTube

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What are the benefits of OCR data extraction?

We have found that OCR data extraction offers numerous benefits for businesses in various industries.

Improving business productivity

OCR technology plays a crucial role in improving business productivity.

When applied to business documents, OCR can enable editable content to be repurposed and used for decision-making processes.

This results in reduced time spent searching for and managing physical documents.

In addition, when businesses automate the data extraction pipeline with OCR data capture, employees don’t have to spend time on tedious tasks, freeing up time and resources for employees to focus on more critical tasks, thus, boosting overall productivity.


Shoeboxed’s OCR technology can be used for tracking business expenses


Shoeboxed can be used by businesses to track business expenses.

Employees can scan or mail in their business receipts and have them uploaded to their Shoeboxed account, where they are human-verified and automatically categorized into tax categories.

The extracted text can create structured data entries by digitizing receipts, making tracking and analyzing costs easier.

This information can be easily generated into expense reports, tax deductions, or proof for an audit.

This process eliminates manual data entry and enables businesses to create digital data from physical documents, such as receipts, business cards, or other documents.

OCR and data extraction saves businesses time and money.

Scalability and growth

Another primary benefit of using OCR data extraction is its potential for scalability and growth.

As a business expands, the volume of documentation and data entry requirements increases.

Implementing OCR technology can handle growing document loads without compromising the quality and speed of data extraction.

This flexibility ensures a seamless transition as the company grows, reducing the need for additional resources while maintaining efficient document management.

Improving customer experience

OCR data extraction also has a significant effect on customer experience.

Businesses can quickly and accurately extract relevant information from documents to improve response times to customer inquiries and requests.

Furthermore, OCR facilitates better organization and accessibility of customer-related papers, providing a more personalized and efficient service.

Businesses can enhance customer satisfaction and foster stronger relationships by streamlining the data extraction process.

By automating data entry processes, streamlining workflows, and improving customer experiences, OCR data capture plays a vital role in promoting business efficiency, scalability, and growth.

Optical Character Recognition by IBM Technology

Frequently asked questions

What are the benefits of using OCR data extraction?

OCR data extraction offers numerous advantages, including faster processing times, reduced manual labor, and improved accuracy compared to manual data extraction. It streamlines workflows and is particularly useful for handling large volumes of documents, thus increasing overall productivity.

How accurate is OCR technology in data extraction?

Accuracy in OCR technology varies depending on the quality of the scanned document, the type of software used, and how well the OCR engine is trained. Modern OCR tools can achieve high accuracy rates. However, poor image quality, unclear text, or highly stylized fonts can affect the accuracy of OCR data extraction.

How do OCR data extraction tools handle handwritten text?

OCR tools for handwritten text use specialized algorithms and pattern recognition techniques to decipher unique, irregular handwriting patterns. These tools have improved significantly in recent years.


It is estimated that 93% of businesses need help locating papers and information, and 46% of employees waste their time on inefficient paper-related tasks.

Both businesses and individuals should use OCR data extraction of business processes and data capture capabilities to ultimately minimize data entry, streamline workflows, enable more informed decision-making, and save businesses time and money.

Caryl Ramsey has years of experience assisting in different aspects of bookkeeping, taxes, and customer service. She uses a variety of accounting software for setting up client information, reconciling accounts, coding expenses, running financial reports, and preparing tax returns. She is also experienced in setting up corporations with the State Corporation Commission and the IRS.

About Shoeboxed!

Shoeboxed is a receipt scanning service with receipt management software that supports multiple methods for receipt capture: send, scan, upload, forward, and more!

You can stuff your receipts into one of our Magic Envelopes (prepaid postage within the US). Use our receipt tracker + receipt scanner app (iPhone, iPad and Android) to snap a picture while on the go. Auto-import receipts from Gmail. Or forward a receipt to your designated Shoeboxed email address.

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