This article explores how to save a failing data extraction project and how best to resolve the problems resulting from changes in documents, image quality and data types.
Data Extraction: How to Hit Home Runs
Hit Home Runs in data extraction by going beyond basic OCR automation and leveraging advanced ICR capabilities
Document Capture is Dead. Long Live Document Capture!
Long Live Document Capture! The latest renaissance in document capture with advanced data extraction and classification is discussed in this timely article.
Ensuring Accurate Data Extraction & Ground Truth Data
Parascript is working with more and more service providers that need to perform data extraction, which must be as accurate as possible to ensure business operations are successful and dependable. This article delves into how service providers best ensure accurate data extraction and ground truth data.
Big Data Quality: What Accuracy Do You Get?
Big Data Quality: What Accuracy Do You Get? Don’t believe the hype about accuracy rates, ensure your vendors prove it.
Data Extraction Best Practices in Document Management
Best practices in document management—when approaching a document management challenge that necessitates data extraction—require fully understanding the types of documents involved. The “nature” of the document is fundamental in determining the most appropriate technologies and techniques to use. For example, OCR cannot provide a comprehensive solution in many cases. Instead, OCR acts as the underlying, supporting technology that aids with producing a final result.
Information Management Project Success Begins with Objectives/Clusters
When it comes to successful information management projects, it’s not the technology that’s the problem. It’s the organizations that purchase and use it. At least, that’s what Laurence Hart maintains in his recent CMSWire article. Now Lawrence is a practitioner, and he’s seen firsthand how information management projects can fail or succeed. Personally, as a […]
Data Extraction: Not Your Average OCR
Going beyond OCR to extract important, context-based data from documents with high reliability and accuracy.
Hidden Costs: Image Quality and Usability
Image quality remains an ongoing challenge for our financial clients. In benchmarking our data verification and check fraud prevention projects, it’s easy to demonstrate that image-based processing is highly successful. However, the very small percentage of poor quality images can cost financial institutions millions of dollars to research and correct. A recent white paper from […]
What is the Difference between OCR and ICR?
There are a lot of abbreviations and market-speak when it comes to the variety of technologies commercially available to solve the image-to-data problem. OCR, ICR, ACR, NHR, etc. Then there are specific applications of these technologies that further complicate things. Abbreviations like CAR/LAR, MICR, etc. So what are the major differences between OCR and ICR and […]
Redefining Capture – It’s Not Just About Paper Anymore
For years, capture systems have focused on scanning paper documents to create digital versions instead. This fundamental scan-and-store approach helps reduce the costs and inefficiencies of paper-intensive processes. But today, organizations are finding new advantages by bringing the concept of capture out of the back office and into the front line of business process. The […]
4 Common Misleading Myths about ICR
According to the recent AIIM research “Shedding Light on the Dark Data in your Document Capture Processes,” the use of OCR, OMR and barcode recognition is the highest level of recognition technology achieved by nearly 80% of survey respondents. While 22% are using some type of handwriting recognition, it is mostly constrained hand print in […]