We have
all become accustomed to accessing data instantly, and data is seen as a
valuable asset not just by businesses but by educational institutions. However,
higher education departments and institutions seem to be a bit behind on the
times relying highly on manual data extraction from emails, printed forms, and
other sources. Not only does it tie up places like the admissions department in
endless paperwork, but the data also isnt easily accessible.

 

There is an opportunity for higher education institutions to speed
up their application process using OCR or Intelligent Data Extraction. In this
article, well briefly describe how OCR can be used by institutions to ensure a
swifter process.

 

Using
Document Capture Technology

OCR, also known as document capture technology, isnt new, but the
industry has become exponentially larger, with new and more advanced tools
becoming cheaper. The added functionality allows higher education institutions
to quickly scan documents and extract the required information thats fed into
a database.

 

The automated system can easily organize all the data, structure
and prepare it to be accessed on-demand. Not only will the use of Intelligent
Data Extraction speed up the application process but it will reduce overall
operating costs.

 

How an Institutions Intelligent Data Extraction Can Work?

 

The
application process will generally have six steps. However, the steps could
vary depending on the requirements of the institution.

·      
Ingestion: The data can be ingested or
captured via scanners, UNC folders, online forms, multi-functional peripherals,
etc.

·      
Image Processing: The step involves normalizing and
cleaning up images which prepares them for classification. Usually, deskew and
despeckle filters are applied to improve the quality. Once completed, the
document is ready for data extraction.

·      
Classification:
 The first step is for the system to identify what type of document is
being ingested via OCR, ICR, and OMR. So, the system will determine if the
document is an admission application, transfer application, fee voucher etc.  Once
a document type has been identified, the next step is extraction.

·      
Extraction:
The extraction process identifies the required metadata within the document.
The data is then extracted based on what it is intended to be used for and
stored in a database.

·      
Validation:
If there is something thats not clear to the Intelligent Data Extraction
system, it is flagged for human review. For instance, documents with blurry
writing, smudges, and spills may require human review.

·      
Delivered:
Documents that are validated can have their information moved to a repository
or a system, which can reside on a local server or the cloud.

Conclusion

 

It is
important to note that the entire Intelligent Data Extraction process i.e. all six
steps, is automated. It is also not hard to see how swiftly multiple documents
can be processed and their information made easily available to the staff who shamrock solutions
can then be responsible for handling other matters associated with the
application process. However, since each institution is different, the way in
which documents are handled and classified will vary.