Handling hundreds of contracts is a stressful and boring task. You need to read all the clauses thoroughly and make sure everything is aligned with your policy. Once you missed a clause, it could lead to a major problem.
Why Use Automation to Extract Contract Terms and Clauses?
No system can handle poor quality data when Optical Character Recognition (OCR) quality (the process of converting image files or paper documents into computer-readable, searchable media) affects all critical characteristics.
Several companies say that machine learning and/or predictive coding can make up for OCR quality. This is not the case at all.
Some OCR errors can be accounted for on any system, but, in fact, high-fidelity systems require as much clean data as possible. We didn’t mean “that machine learning … can compensate for OCR quality” (although we think it is possible), but rather that a machine learning-based contract metadata extraction system can often identify clauses in low-quality scans.
Even though many words were changed during the OCR process. We agree that clean scans are preferred. But when viewing contracts, you often come across low-quality scans, and contracts must be viewed in whatever form they are in.
See also: 5 Easy Hacks to Improve OCR Accuracy
There are compelling theoretical reasons why a machine learning system should outperform search engines in delivering keywords and database contracts when crawling low quality (and in general). A recent academic study “suggests that statistical machine learning is the best approach to solving information extraction problems.”
However, since many previous help posts have described the pros and cons of keywords (see also the keyword analysis example here), comparing and reviewing machine learning conventions, we thought it might be more helpful to show how our system performs at low crawling. quality that has not been trained.
See also: What Is Machine Learning? All Your Questions, Answered
How to Extract Contract Terms Using OCR?
To properly explain why low-quality scans can be a problem for contract abstraction software, it is important to first explain the suitability of OCR software. See how our bot extract contract documents using OCR and Machine Learning:
Contract metadata extraction software like ours works by applying a contract delivery template to the text. If an image file (such as a scanned image) is loaded, the system must convert the scanned image to text.
Optical Character Recognition (“OCR”) software performs this conversion of an image to text. Contract validation software vendors like us usually (probably exclusively, but I can’t say for sure) integrate third-party integrated OCR into their systems for this functionality (sometimes with OCR modifications).
After converting the document to text, the contract metadata extraction system can apply its clause extraction templates to the text and record the results accordingly. OCR accuracy can be degraded by poor-quality scans, and there are many poor-quality scans in the contract overview.
This means that clause extraction templates often have to be used for text, as opposed to anything they were explicitly created for.
By: Elsa Ajarwati
Attribution: Business photo created by pressfoto – www.freepik.com
