As a business, do you work with large quantities of PDF files?
Do you have to collect data from pdf forms ensuring all the data is saved into the database unmodified/unaltered?
Another question - are doing it manually?
If yes, only you can imagine how time-consuming and error-prone the whole process can be!
Manual data entry, if used in a high-speed data processor environment makes the system inefficient and the built-in queue defeats the entire essence of process management to improve performance and system productivity. Here’s how manual PDF processing keeps your business to reach its full potential:-
The manual procedure is carried out by people who can't do routine activities unfailingly. It is most likely that the person might commit a mistake. These fat finger errors can mostly be classified into two categories:-
i) Transcription errors - These errors are usually associated with transcribing words that include typos, deletion, repetition, or spelling errors
ii) Transposition errors - These errors are usually associated with numerals when you input numerals in wrong order. For example, instead of 567, you input 576 by mistake.
With no verification layer, manual data entry can have an error rate as high as 4%. That means 400 errors in every 10,000 words. As you work with a larger data set, this error rate can increase to 5% or more.
A human cannot compete with the computer when it comes to processing time and accuracy. Concerning extracting data from PDFs involving millions of objects, the low-speed design of the manual processing is checked for the integrity and validation of data so that the data element that enters a system is accurate.
When processed manually, each document can take up to 10-15 minutes to accurately extract data, review, and store in a structured database. For larger pdf files, the processing time can easily go up to 45-60 minutes.
Slow manual processing makes the overall process too costly to sustain. Let’s say, you invest $20 per hour per person in manual document processing. If a person takes 10 minutes to process one single document, the cost to process a single document turns out to be $3.33.
Add the cost of an additional verification layer to the whole process, and the cost goes even higher.
In a system, where data protection is a concern can be severely affected by manual data entry. Sensitive documents may grow legs and move, thus compromising the whole scheme. For businesses, confidentiality is their utmost priority. As high as 75.33 % of the data can be lost/leaked during manual pdf document processing which can put the company at risk.
Document processing is a crucial aspect of many businesses. Let’s have a look at these businesses and the list of documents they need to process on a regular basis:-
These documents are often shared via email, in pdf or scanned image format. As the next step, the extraction of data can be done either manually or using automated processing methods. More and more businesses are gradually adopting automation in their data entry procedures, with the BFSI sector being the front runner. The BFSI sector dominated the Robotic Process Automation market share with more than 29% of global RPA revenue in 2019 as per the report published by Grand View Research.
The BFSI sector is closely followed by Pharma and Healthcare. If 2020 is any indication, the healthcare and logistics industries are going to all set to adopt automation at a much higher scale.
Taking into account the limitations of manual data extraction, businesses are now keen to employ automated PDF data extraction software to process and analyze data from PDF documents/scanned images with minimal human interference.
Extracting a portion of information from most other text formats such as JSON, XLS, or CSV is easy as these formats are built for data processing but extracting selected text from PDF is difficult. Here are some of the limitations of data extraction from PDFs:-
The major drawback of automated extraction is its inability to read and collect data from raster pdfs. For example, you need to maximize the size of images above 1000 for high-resolution scans. Hence, vector pdfs are necessary for extraction. It requires more operator involvement and manual cleanups. Not only this, when raster pdf is run through software, the flat image will be converted into a tracing layer for manual work.
Analytics and tables help businesses by providing an overview of their performances. The insights provided by tables help companies to optimize their business and come up with efficient ways to make better decisions in the future. Unfortunately, automated pdfs show that the table data is invalid and require manual suggestions to correct them.
Docsumo provides a friendly and easy-to-understand interface for PDF data extraction.
Here are the steps to follow for data extraction from PDF successfully.
Enterprises need to handle several PDF files in a day. But sometimes mishap happens, particularly when translating scanned PDF documents into Excel. These restrictions are faced by various applications.
For that, Docsumo has developed a data extractor that helps the user to extract data from pdf forms including scanned and unscanned pdf files by converting them into Excel and other formats.
With our free PDF data extraction tool, you can get your specified data converted within seconds. It’s user-friendly and the seamless experience has engaged many customers across the globe. If you are looking for a fine data extractor, Docsumo will be the right one for you.
In today’s dynamic business world, filing and archiving official documents in the digital form makes it handy, and works wonders in the future or in unforeseen circumstances.
Optical Character Recognition (OCR) is the technology to convert an image of text into machine-readable text. It is the underlying technology for various data extraction solutions including Intelligent Document Processing. However, OCR is not smart enough to figure out the context in a document - it works simply by distinguishing text pixels from the background and finding a pattern. This limitation could cause inaccuracy in captured data that could directly impact the output of your data extraction model.
Accounts payable is a key financial function for any business. Corporations can have thousands of suppliers; even for relatively smaller businesses, the number of suppliers could be in hundreds. All the invoices they receive from these suppliers come in multiple formats, layouts, and templates - some semi-structured, some unstructured. Therefore, firms expend time and resources to capture invoice information through manual data entry and verification of accounts payable. Manual data entry is not feasible in the long run, definitely not on a large scale. Before we talk about how intelligent invoicing solves the problems associated with manual invoicing, let’s discuss the challenges in much detail.
As most of an organization's information is available in an unstructured format, processing it requires an automated system that can handle documents with minimum human interaction. OCR is one such technology, but its scope is limited as it requires human interaction and is highly dependent on the layout and structure of the document to be processed.These limitations are overcome by Intelligent Data Extraction.Using artificial intelligence, the Intelligent Data Extraction technology extracts data from documents and transforms it into useful information through the extraction process. It functions as a singular tool for extracting information from any type of document and aids in optimizing company operations.