Have you ever needed to extract data from a PDF or scanned document into a spreadsheet? OCR can be a real time saver. Simply scan your documents and convert images into editable, searchable text. OCR makes data extraction easy, whether working with PDF files, photographs, or scanned pages.
This guide will walk you through the OCR to spreadsheet process, from scanning to improving accuracy. We will recommend OCR tools and provide tips to improve accuracy and real-world OCR use cases that save hours of manual work.
Why rearrange data in spreadsheets with OCR?
OCR totally changes the rules of the game. It takes the data saved in your scanned documents, PDF files and photos and converts it into structured data. We are talking about ready-to-use spreadsheets. This opens up a whole new world of possibilities.
Below are some reasons why you should consider using OCR to organize your data in spreadsheets:
1. Data analysis made easier
Once data is extracted and clearly organized into rows and columns in a spreadsheet, it becomes much easier to analyze and work with. You can quickly spot trends, sort, filter, use formulas, and create pivot tables and charts. This level of data manipulation is not possible on scanned or PDF documents.
2. Better data quality
Converting OCR to spreadsheets gives you clean, structured data. Data can be validated and standardized during the OCR process. This improves overall data quality and accuracy compared to unstructured scanned documents.
3. Improved search capability
Searching for scanned documents and images is complex; OCR solves this problem by converting images into real text. Once in a spreadsheet, the data is completely searchable. You will be able to instantly find what you need.
4. Improved data sharing
Spreadsheets containing extracted data can be easily shared with others for collaboration. The data is now in a standardized reusable format instead of being trapped in individual document images.
5. Automation capabilities
Spreadsheet data can be automated and optimized across business systems. With the ability to generate CSV files, data extracted by OCR can automatically flow into databases and other line-of-business applications.
6. Skip manual processing
Your team will no longer need to manually transcribe data from scanned documents or endure the tedious and inefficient workflow of copying and pasting PDF files. You can reduce errors and save time cleaning and validating data by eliminating monotonous data entry tasks. As a result, your staff can dedicate their efforts to more productive and satisfying work.
7. Scalability
OCR conversion increases as data volumes grow. Whether you need to process hundreds or even thousands of pages of documents, OCR automation handles it seamlessly. Manual data entry does not scale as quickly for large volumes.
The OCR to Spreadsheet Workflow
Converting documents to spreadsheets with OCR is easy if you follow these key steps. By setting up an efficient workflow, you can save hours of manual data entry and quickly access information saved in PDF files or scanned files.
Let's dive in.
1. Gather documents for OCR
First, collect the document images, PDF files or scanned documents that contain the data you need to extract. Nanonets allows you to easily import files from multiple sources, including email, cloud storage, Dropbox, Google Drive, OneDrive, and more.
You can also set up automated watch folders or email to automatically process any new files or incoming attachments. API calls and integrations with other business software can also be configured for seamless data extraction.
2. Define data fields
Next, specify the data fields or columns you want to extract, such as invoice number, date, customer name, amount due, etc. Nanonets offers different ai models for document types like invoices, receipts, business cards, and more.
Pre-built models already know how to intelligently extract common fields from each document type. You can also configure your own custom fields and train the ai model. Then you can prepare the model with some samples. Simply draw zones on sample documents to determine where critical data resides.
You are now ready to run OCR and extract data from your documents. Nanonets leverages advanced ai and ML algorithms to automatically identify and capture text from complex document layouts with high accuracy. The ai ”reads” each document, extracts the defined fields and generates structured data ready for export.
This step is completely automated for you once the data fields and ai model are configured correctly. Behind the scenes, OCR technology converts scanned images into text. Smart Zone Detection then selects the relevant data fields.
4. Validate and correct data
Review the extracted data for accuracy. Nanonets makes this easy by allowing you to make corrections directly in the document viewer. For more advanced users, they can also edit the structured JSON output.
You can also use automated validation capabilities to configure rules to validate captured data. For example, you can check whether a date falls within a valid range or a numeric value below a threshold. Any validation issues are flagged for review.
5. Export and integrate spreadsheet data
The final result containing the structured data extracted from your scanned documents or PDF files can be downloaded and used for further purposes. Nanonets allows you to export it as a CSV, Excel, or JSON file, allowing you to easily import the data into your preferred spreadsheet application or other business software.
It can also integrate directly with popular apps like Google Sheets, QuickBooks, Salesforce, etc. Zapier integration allows you to connect with over 5,000 apps for seamless data flow. This integration ensures that your data is automatically updated across all your platforms in real time.
How to improve the OCR to spreadsheet process
OCR technology is not perfect. Sometimes you may run into problems with low-quality scans, complex layouts, or unusual fonts. But even small, marginal improvements to the OCR process can result in significant time and cost savings.
Suppose you run an insurance company that processes thousands of documents per day. Even a 2% improvement in OCR accuracy can save hundreds of hours of work per week.
Here are some ways to improve the spreadsheet OCR process:
1. Improve the quality of your scans
Make sure the documents you are scanning are clear and legible. Poor quality scans can cause errors in the OCR process. Therefore, pre-process scans to improve image quality before feeding them into your OCR system.
Tips to improve scan quality:
- Use a high resolution scanner (at least 300 dpi). This captures finer details that can help the OCR engine recognize characters accurately.
- Make sure the pages are aligned correctly and not crooked. Bugfix fixes skewed scans.
- Check the brightness and contrast of the scan. Adjust the levels so that the text is clearly visible and not too light or dark.
- Clean the scanner glass to prevent dust, smudges, or artifacts on scanned images.
- Use Adobe Scan or similar apps to capture high-quality scans with your smartphone.
- Use image enhancement techniques such as sharpening, noise reduction, and binarization.
2. Standardize your documents
Consistency in document layout and design can significantly improve OCR accuracy. If possible, standardize the format of the documents you process. This means keeping data fields in the same location in each document, using consistent fonts and sizes, and maintaining a clean, uncluttered layout.
Here are some tips for standardizing documents:
- Use a consistent template for all documents of the same type.
- Keep essential data fields in the same place in each document.
- Use clear, legible fonts and avoid artistic or unusual fonts.
- Avoid clutter and keep the design clean and simple.
- Limit the use of images, logos, and graphics near important text fields.
- Use high-contrast colors for text and background to improve readability.
<h3 id="3-invest-in-an-ai-powered-ocr-system”>3. Invest in an ai-based OCR system
These systems use machine learning algorithms to learn from each document processed, continually improving their ability to recognize and extract relevant data.
Nanonets are an excellent example of an ai-powered OCR system. It offers pre-trained models for different types of documents and allows you to customize the model according to your needs. The more data you process, the better you will recognize patterns and extract data accurately.
Additionally, the language recognition and context understanding capabilities of ai-powered OCR systems allow them to handle documents in multiple languages, currencies, tax formats, and more. This makes them very versatile and adaptable to various business needs.
4. Set up automated workflows
Automating repetitive manual steps in your OCR workflow can improve efficiency and minimize errors. For example, you can set up automatic import rules that ensure that the OCR system automatically processes each invoice sent to [email protected].
Integrations with enterprise software like ERP enable seamless data flow. Data extracted from the spreadsheet can be automatically synchronized with downstream databases. Automated validation rules help detect any extraction errors early. Workflows can route documents that need review to the appropriate personnel. Automatic notifications and reminders ensure no deadlines are missed.
Final thoughts
OCR technology has revolutionized the way we extract and work with data from scanned documents and PDF files. By converting images into structured spreadsheet data, OCR eliminates tedious manual entry and improves analysis capabilities.
As outlined in this guide, creating an efficient OCR workflow with the right tools, like Nanonets, can save enormous amounts of time. Minor improvements in accuracy also quickly translate into significant savings.
Want to see how OCR can speed up your company's workflows? Nanonets offers a free version to try extracting data from your documents using ai. Converting PDF tables or scanned invoices into editable Excel sheets has never been easier. Sign up now to get started!
