Smart and efficient way for businesses to extract data from pdf & boost productivity

Smart and efficient way for businesses to extract data from pdf & boost productivity

Portable Document Format, commonly known as PDF files have become ubiquitous since it was introduced in 1993. PDF was designed by Adobe in the 90s with the goal to make any file look exactly the same no matter what screen you see it on.

PDF files are widely used in businesses because of their versatility and ease of use. PDF files offer ease in terms of simple viewing, printing, and navigating. Industries such as insurance and lending rely heavily on the pdf file format to collect data from their customers. This collected data has to go through different layers of processing and pdf files are converted to different structured formats such as CSV, Excel files, or JSON before they could be processed.

Why do businesses use pdf?

  • Fixed Document Format:PDF format files leave the document unchanged despite the type of device, computer, and operating system used.
  • The Universality of Format:It is easy to share PDF files across multiple operating systems without altering the document’s content. It helps ensure the accuracy of the documents shared. Moreover, PDF files are accepted all around the world which offers the added advantage of universality.
  • Document Security:When working with sensitive data, like credit card information, it is important to ensure data or information security. User-password-protected PDF files can avoid unauthorized access.  It also detects if the document has been edited or opened by unauthorized persons, ensuring security.

Why extract data from PDF files is so difficult?

The main issue is that a PDF document carries no markup or hierarchy of data. The problem is even more complicated when it comes to images (PNG or JPG) or images converted to PDF format. In the case of scanned pdf and images, the character level data is also lost and needs to be recovered using OCR which is never 100% accurate.

In both PDF and images, the information about what the data represents needs to be interpreted in order to convert it into a structured format. However, pdf format is unstructured, making it difficult to access the information for data analysis.

Unlike other documents, such as Excel spreadsheets, PDFs do not have a standard format. Therefore, it is challenging to extract data from pdf structure and understand the data within them. Using PDF extraction or PDF scraping software to extract data from pdf to database is more of a necessity for organizations that deal with a large number of source PDFs and do not want to deal with manual pdf data extraction to excel.

Which are the industries that benefit highly from pdf scraping?

PDF scraping is highly valuable in the healthcare, financial, and automotive sectors. They have large sets of printed datasheets that needs to be analyzed making pdf extraction crucial. Without PDF scraping tools, digitizing this enormous amount of data can take days and directly impact the organization’s bottom line. Hence a software to extract data from pdf files to database, has become really important in this day and age.

For example, most organizations struggle to extract data from PDF to excel. Therefore, the most common approach that businesses take is to manually re-key the data in the destination system. However, manual data entry is a tedious, costly, and error-prone process. Additionally, this approach is inefficient as most businesses process hundreds of PDF files each day. Re-entering the data will require you to have a team that continually works on this day in and day out.

The alternate approach is to code and develop in-house software to extract data from PDF documents. This is a comparatively better approach, but it comes with its own set of challenges, including capturing data from scanned documents, catering to the countless different formats, and transforming the data into a structure compatible with your storage system.

What is optical character recognition (OCR) technology software?

Optical character recognition (OCR) technology is a business solution for automating data extraction from printed or written text from a scanned document or image file and then converting the text into a machine-readable form to be used for data processing like editing or searching.

OCR solutions improve information accessibility for users. A common application of OCR technology is the automated conversion of an image-based PDF, TIFF or JPG into a text based machine-readable file. OCR-processed digital files, such as receipts, contracts, invoices, financial statements and more, can be:

  • Searched from a large repository to find the correct document
  • Viewed, with search capability within each document
  • Edited, when corrections need to be made
  • Repurposed, with extracted text sent to other systems

How automated OCR capabilities for data entry benefits business operations and workflows

Businesses that employ OCR capabilities to convert images and PDFs (typically originating as scanned paper documents) save time and resources that would otherwise be necessary to manage unsearchable data. Once transferred, OCR-processed textual information can be used by businesses more easily and quickly.

The benefits of OCR technology to businesses include:

  • Elimination of manual data entry
  • Resource savings due to the ability to process more data faster and with fewer resources
  • Error reductions
  • Reallocation of physical storage space
  • Improved productivity
  • Centralize and secure data (no fires, break-ins or documents lost in the back vaults)
  • Improve service by ensuring employees have the most up-to-date, accurate information when they need it
  • The better that the OCR is, the more text is extracted, drastically cutting data entry, speeding up work, and giving you more data to make wise decisions.
  • However, many software use sub-par capture that doesn’t accurately recognize a high percentage of text and leaves a lot to be desired.

Using OCR: How Accurate is Your Data?

Leveraging Your Document Data

Obviously, the accuracy of the conversion is important, and most OCR software provides 98 to 99 percent accuracy, measured at the page level. This means that in a page of 1,000 characters, 980 to 990 characters will be accurate. In most cases, this level of accuracy is acceptable.

What about putting data from documents to good use by extracting specific data and tagging it so it can be added to a database or be used as metadata describing a specific document? Operations such as accounting rely upon accurate data from invoices (such as the invoice number, date, quantities of items purchased, and taxes).

Does the 98 to 99 percent accuracy of full-page OCR translate to an adequate level of accuracy on data extraction from these documents? Absolutely not.

Accuracy Guaranteed: What It Means

If you need to obtain 99 percent accuracy at a data field level, then relying on 99 percent page-level accuracy could lead to disaster. For instance, in the case of our 1,000-character page, although an OCR engine might have 99 percent accuracy at the page level, what if those 10 erroneous characters are within 10 of the 20 data fields required by the business?

Suddenly, this 99 percent accuracy drops to 50 percent accuracy. This is where field-level accuracy comes into play, using what’s known as the field-level confidence score.

Also keep in mind that page-level accuracy rates are often based upon good-quality scans. If your organization has to deal with faxed documents or documents that have hard-to-read fonts such as from a dot-matrix printer or the document is printed on a pre-printed stationery causing overlapping text or document photographed in bad lighting conditions etc, page-level accuracy can be much lower.

Use of AI/ML in improving the accuracy of OCR

AI/Ml can boost up the accuracy of OCR techniques.  KlearStack team has developed a novel data capture method that can identify characters and images with 99% accuracy.

KlearStack platform pre-processes the documents using latest Machine Learning techniques that can handle a variety of document formats for a given document type (e.g. various formats of invoices or purchase orders etc). The ML models pass the data through various layers of transformation till the final stage. At each step special care is taken to preserve the data in its original form. The ML models are a combination of different algorithms combined together to improve the accuracy.

Conclusion

Ashutosh Saitwal
Ashutosh Saitwal
www.klearstack.com/

Ashutosh is the founder and director of the award winning KlearStack AI platform. You can catch him speaking at NASSCOM events around the world where he speaks and is an evangelist for RPA, AI, Machine Learning and Intelligent Document Processing.