Guide to OCR Data Extraction for Enterprises: Types, Technologies, Benefits

According to a study by Oracle, approximately 90% of businesses believe data analytics can improve their decision-making process by providing insights for better decisions regarding supply chain and finance. 

Peter Sondergaard, VP of Research at Gartner Inc., says, “Information is the oil of the 21st century, and analytics is the combustion engine.”

However, with the emergence of OCR in the 1960s, extraction became complex. OCR, or Optical Character Recognition, is a technology that turns images of text into an editable format. Their consistent formatting leads to more accuracy and interpretation errors. This, along with a 1% error rate in manual data entry, makes OCR data extraction a struggle for enterprises.

New technologies like AI and ML have evolved to tackle this problem. In this blog, we will dive deep into the concept of OCR and data extraction techniques.

What is OCR Data Extraction? 

Optical Character Recognition (OCR) is a technology that bridges the gap between the human and digital worlds. It is an efficient way to digitally store physical documents in scanned or image format, which computers can understand.

In short, OCR has made storage and access to physical documents easier. Here’s a breakdown of this technology's workings:

1. Image Preprocessing: It prepares the image for analysis
2. Character Segmentation: Divides the image into smaller components, separating characters from the background. 
3. Feature Extraction: Analyses each character to identify key features.
4. Pattern Recognition: Attempts to match extracted features with its vast library of patterns. 
5. Machine Learning: Utilises large sets of text and image pairs to improve accuracy. 

With the rise of OCR, data extraction from them is essential for analysis. According to Trade Finance Global, 28% of banks already use OCR data extraction and create clickable documents. It unlocks textual content along with easy analysis, organization, and utilization. 

How does OCR Data Extraction work?

Data Extraction through OCR streamlines transforming scanned documents into usable data. Ensuring 99% accuracy and 10x  more speed than manual entry. Here are the key steps involved:

a. Image capture

This is the starting point where the source of text gets used. The image can be captured in 2 ways:

• Scan: Documents are fed through a scanner to create a digital image file of content. This method is beneficial for paper-based forms, receipts, or historical documents. 
• Upload: This method is handy if the file is already in digital format. One can directly upload the file (PDF or image) to the software. 

b. Text recognition

The OCR software captures the image and preprocesses it to adjust factors. Then, it is divided into smaller components to separate characters from the background. The software identifies key features like lines, curves, and endpoints. This helps match extracted features with known characters to recognize text. Finally, machine learning is leveraged to improve accuracy. 

c. Data extraction

Post-text recognition is the process focused on extracting specific information. It involves:

• Template matching: Specific useful data points reside within the document. Thus, the software uses a predefined template or layout to identify these. For instance, a template for invoices identifies sections labeled "Customer Name", "Invoice Number", or "Total Amount".
• Keyword spotting: Certain keywords and phrases also signify crucial information. These help pinpoint relevant information within the extracted text. 

d. Data formatting 

Finally, the software organizes extracted data into a usable format for analysis and integration. Some OCR data extraction methods are:

• Structured Output: Extracted data is put into a table or database with designated fields for each data point.
• Text Summarisation: Based on needs, data may be summarised or converted into different formats like plain text files. 

Document that can be processed using OCR Data Extraction

OCR data extraction has the power to breathe new life into documents. It can turn static images into usable data banks. However, the use cases are limited. Here are some common documents it can tackle: 

a. Invoices & receipts

These documents often feature a standard format and can be considered semi-structured. This makes them easy to scan and ideal for data extraction. 

An invoice contains data points like vendor name, date, line items, and invoice number. OCR software can easily extract these through template matching and keyword spotting, eliminating manual data entry time and potential errors. 

b. Business card 

Although small, business cards are invaluable for enterprises. These semi-structured documents contain essential details like name, title, contact details, etc. 

One can extract this data by using OCR data extraction. The extracted data is automatically populated in CRM systems. In turn, it saves time when building business networks.

c. Forms

Tax, ACCORD, and Medicaid forms are commonly used documents in enterprises, and they can be structured or semi-structured. They come in various formats, from strictly structured to semi-structured with open-ended questions. 

OCR software is trained to extract pre-filled data from these forms. For semi-structured forms, keyword spotting helps to identify answers. Overall, OCR can streamline data entry for various administrative tasks.

d. Legal documents (contracts, agreements) 

OCRs can’t yet fully decode the legal nuances of contracts. However, it can extract key data like names, dates, and reference numbers. This data is crucial for indexing and search purposes. Legal professionals can use OCR to locate details in vast documents quickly.

These documents are likely to be unstructured and hard for OCR to scan. 

e. Handwritten notes (limited capability)

Handwritten text is a major challenge for OCR data extraction, as it can be unstructured. However, advancements in machine learning are improving accuracy. OCR can extract keywords using clear and consistent handwriting. However, complex handwriting with diagrams still needs to be made available and requires human intervention.

Technologies used in Data Extraction

OCR data extraction has substantially improved accuracy, interpretation, and turnaround time. This is due to the integration of the latest technologies. Here’s a breakdown of the technologies that fuel the process:

a. Machine learning

ML plays the most crucial role in OCR data extraction. It analyses large text and image pair datasets to improve the software’s abilities.

These algorithms ensure the character recognition power increases for handwritten and unclear text over time. Thus, the more data it processes, the better it understands complex variations. 

b. Artificial intelligence

AI is a broader term that encompasses machine learning and advanced techniques. It helps automate the entire process of OCR and data extraction. From image preprocessing to extraction and formatting, it can handle everything. Ultimately, it eliminates the need for manual interventions and streamlines workflow.

c. Computer vision

This empowers OCR to see beyond just characters. It can identify the document's layout, including tables, logos, etc. With this, the OCR engine can better understand the context of the text. And extract data with greater accuracy. 

Top 5 examples of Data Extraction

The global OCR market value will boost at a CAGR of 16% by 2023. This shift is already in motion as many enterprises and businesses have taken up OCR data extraction. Listed below are some everyday uses of OCR:

a. Automating invoice processing

Traditionally, invoice data was manually entered and analyzed. The process was extremely tedious and error-prone. With OCR, it has streamlined as data points in invoices are automatically extracted. This saves time, leading to faster payouts and improved cash flow. 

b. Extract financial data from reports

Financial analysis needs data from different reports for effective performance. OCR helps pull out this data from statements and market reports. This allows professionals to focus on analysis and make informed decisions. 

c. Collecting survey responses

Survey data often has unique responses from each participant. It requires manual reading and coding responses from each form. OCR automatically extracted respondent information and their answers. In turn, researchers can quickly analyze data and gain insights. 

d. Process loan applications

Loan processing is often bogged down due to manual interventions. OCR helps extract crucial information from application forms, including name, income, and loan amount. It expedites processing time and allows lenders to make quicker decisions. Additionally, accurate data reduced application delays. 

e. Review insurance claim processing

Insurance claims require data extraction from submitted documents like police reports or medical records. OCR can fasten this process by capturing relevant data. Thus, insurance companies can offer quick claims and acquire customer satisfaction.

Benefits of OCR Data Extraction

OCR has many benefits for enterprises and businesses. The top advantage is a reduction in data entry and errors. This allows them to invest more effort in analyzing the data. Several other major benefits of OCR data extraction are listed:

• Increased efficiency: OCR eliminates the need for manual data entry, reducing the chances of error and the time consumed in entry. It also automates data extraction, streamlining workflow in various departments.
• Improved accuracy: OCR entry doesn’t make typos or miss information unlike manual entry. Rather, it minimizes these errors by capturing data directly from the source document. This ensures reliable and accurate data for decision-making. 
• Cost savings: OCR reduces the need for manual labor in data entry tasks. It also digitalizes paper products, reducing storage space and associated costs. Overall, it helps cut costs in many areas. 
• Enhanced data security: The key role of OCR is to convert paper documents into digital formats. This helps in better storage and security compared to physical options. One can also control access to documents and implement restrictions. 
• Scalability: Humans have a breaking point, but OCR hasn’t. It excels at processing large volumes of data. Be it stacks of invoices or collections of survey forms. It can easily process these without compromising on quality. Thus, it can easily deal with a constant influx of data. 

How OCR enhances data workflow?

Now that the use cases and advantages of OCR are clear, let’s look at how it integrates into a workplace and enhances data flow. Imagine a busy accounts payable department drowning in paper invoices. 

a. Before OCR: Manual data entry creates bottlenecks and delays.

If they choose the traditional method of data entry, they’ll face the following circumstances:

1. Physical Mail Delivery: Invoices will be sent via physical mail, which can lead to backlog.
2. Manual Sorting and Stacking: Employees need to sort and stack invoices manually.
3. Data Entry: Manual extraction and entry of data points like name, number, and line items. It will slow down the process and make it error-prone. 
4. Delayed Processing & Payments: Due to manual intervention, the process will be time-consuming and lead to delays. 

The manual approach often creates bottlenecks, strains resources, and allows room for error, which can disrupt the financial flow. 

b. After OCR: Automated data capture frees up resources and improves accuracy

The second scenario is they use OCR to capture data. Here’s how the scenario will transform: 

1. Digital Capture: Employees will perform electronic scanning and uploading of invoices. This will eliminate the need for physical mail handling. 
2. Automated Data Extraction: The software will automatically extract relevant data points from the scanned invoices. Some methods used will be pattern recognition and pre-defined templates. 
3. Data Import and Integration: The data extracted will be seamlessly imported. Then it can be integrated into the accounting system. 
4. Exception Handling: The system can flag exceptions in case of unclear handwriting or complex layout. This can be sent for human review. 
5. Faster Processing & Payments: Invoice processing time will decrease significantly. This will lead to quicker payments and improved cash flow. 

Thus, integration of OCR in workflow can free up resources, improve accuracy, and reduce time. It bridges the gap between paper-based and digital document transformation. 

Take a look at how Docsumo API can help you with your data extraction

Challenges in OCR Data Extraction

Though powerful OCR comes with its own set of challenges. Some common limitations faced in OCR data extraction are:

a. Poor Document Quality

Blurry documents with skewed images in low lighting cannot be adequately processed. Faded ink, stains, or background clutter also affects OCR’s character recognition.

b. Complex Document Formats

Highly customized layouts, unusual fonts, or non-standard structures often need to be revised. OCR data extraction is best when a defined layout is used. Otherwise, it requires human intervention for accurate extraction. 

c. Handwritten Text Recognition

While OCR with ML has made strides in handwriting recognition, it’s still less accurate. The variable handwriting steps with a cursive script can lead to errors. 

Conclusion: Using OCR for Data Management

According to Dean Abbott, Co-founder of SmarterHQ, ‘No Data is clean but most is useful’. All the data that a business receives is in different formats and contains various values. Some of it is useful, while others are not. 

Manual data sorting and extraction can be time-consuming and error-prone. This is where OCR comes in; it scans, sorts, and extracts data efficiently so that only the most useful insights can be gleaned for business decision-making. Docsumo leverages OCR technology to empower businesses through data extraction. 

Talk to an expert now to upgrade your data extraction techniques.

Additional FAQs: OCR Data Extraction

a. What types of documents can be processed with OCR?

OCR can process various documents, including invoices, receipts, forms, business cards, and legal documents. Documents with a template are easily processed.

b. Is OCR accurate for handwritten text?

The accuracy of handwritten text OCR data extraction is lower. However, having clear writing and proper software configuration can still be beneficial. With the integration of ML, OCRs are continuously training to improve accuracy. 

c. How does OCR improve data security?

OCR improves data security by converting physical documents to digital format. This allows better access control (restricting who sees what) and reduces the risk of losing documents.