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Why OCR Extraction Tools Do Not Do Enough To Remove Manual Document Processing

Document automation can bridge the gap between file cabinets and end systems for offices.

Document automation tools can be a boon for modern businesses. These tools help eliminate manual work from the most tedious, time, and labor-intensive office processes. Any functioning business is expected to handle hundreds of important documents like vendor receipts, contracts, and memoranda at a time. Manually storing, arranging, and retrieving these on-the-fly becomes practically impossible after a while. Automated document processing software saves you the trouble by executing all these tasks. 

But is it a hundred percent accurate at its job? Not always. Document processing technologies like Optical Character Recognition (OCR) help you automate document processing, but not entirely. These technologies are usually too slow and inefficient to be competent and often need manual assistance. This defeats the very purpose of automation. When employees are required to sideline their work and assist automation software in document processing, it ends up making the entire process too slow and labor-intensive. This hinders the business’s ability to use all of its data to its full potential. Here’s why most OCR tools are inefficient for automated document processing. 

The drawbacks of using an OCR tool

Optical Character Recognition or OCR is a computer vision system that converts documents or images of documents into computer coded language. The computer can then read these documents character by character and perform a variety of tasks on them. Originally, OCRs were meant for a single user-specific application - text-to-voice translation for visually impaired users. Over the years, iterations of the system expanded to include other features like intelligent document processing, but its capabilities remain limited. Some of the most pressing drawbacks of OCRs are: 

  • Inefficiency with handwritten text 

Modern OCRs use simple machine learning algorithms to identify and register characters. Text from different font styles can be fed to these algorithms during training to familiarize them with a wide variety of printed characters. However, with handwritten documents, the process isn’t so straightforward. Even with a huge training dataset, it is difficult to train an ML model to accurately read and register handwritten text. Unlike printed text, handwritten text might not follow a specific homogenous font style which makes it difficult for the OCR to read it accurately. Even the most intelligent of OCRs require some amount of manual assistance while scanning handwritten text. 

  • Excessive storage requirement 

OCRs work by capturing a high-resolution image of the document to be scanned. This image is then pre-processed, i.e., cropped, readjusted, and cleaned for any obstructions that might make it difficult for the system to scan a character. The final image is then converted to a bitmap which is analyzed to generate computer code. This tedious process ends up taking up a lot of storage space. This defeats one of the main objectives of intelligent document processing - to optimize memory and reduce load on the system. 

  • Variable efficiency and output quality 

The quality of your original document decides the performance of your OCR system. If your original document is clean and high-resolution, your OCR can scan and analyze it fairly quickly. Blurry, handwritten documents, however, make the process much slower. If the input quality is low, pre-processing ends up taking a lot of time because of OCR’s visual nature. Real-world documents cannot always be expected to be clear, high-quality images. This affects the efficiency of OCRs. 

  • Errors in formatting 

This is perhaps the biggest disadvantage of using OCR. OCR tools are prone to several formatting errors, which might render the output document unreadable. The output from OCR can sometimes be printed in a different font and formatting than originally intended. Formatting these documents correctly requires a lot of manual assistance, which ends up bogging the entire document organization process.  

Why 100% automation is important 

Intelligent document management is implemented by businesses to help streamline their tedious storage, organization, and retrieval processes. The end goal is to save the time and manual labor required to handle thousands of business documents simultaneously. Inefficient automation results in error-riddled output which needs to be checked and verified manually. This means that your employees will be required to personally overlook the storage and classification of each new document despite having an automation system at their disposal. Fully automated systems like ADEx, on the other hand, require zero manual assistance. This helps your business harness the true power of document intelligence without having to spend extra resources on it.

Click here to learn more about how ADEx can fully automate your document processing systems.

Arian Nemati

Co-Founder & CEO

Arian Nemati is a serial entrepreneur and investor for the proptech and real estate industry. Currently CEO of ADEx and active member of YPO.