Abstract:
In
drug manufacturing, keeping track of data is crucial for the drug’s approval
from the FDA. The “Continued
Process Verification” (CPV) data needs to be maintained for ensuring that
the product outputs are within predetermined quality limits. In spite of rising
demand for the creation of digital data directly at the source itself, some
companies follow the traditional methods of documenting the processes
parameters on paper, on designed forms. This leads to data being inaccessible
for others unless it is again digitized by someone. The traditional way of
achieving this is to have someone enter the data into a computer system by
shuffling around the pages in the document. The manual process consumes a lot
of time and leaves very little time for the data entered to be validated. This
article briefly describes the possible methods of automating the manual data
entry process, and how the upcoming technologies can be used for this work.
Introduction
Converting
handwritten and typed data from papers into digital formats is one of the most
commonly faced challenges across industries today. Keeping data on papers has
its own set of limitations like limited accessibility, searchability, using
data for analytics, etc.
Digital
Transformation of Documents
The
digital
transformation of such documents is necessary. Over a while, companies have
adopted various methods of converting this data into a digital structured
format. Some companies hire interns to manually enter the data from the
document into excel sheets or word documents, while some companies need the
scientists working on the projects to manually enter this data into digital
documents. The manual data entry process consumes valuable time and effort from
the scientists while explaining the entire process of data entry to new interns
consumes time from the team. With advancements in the software industry, there
have been multiple attempts to solve these problems, but each solution comes
with its own set of limitations.
Robotic
Process Automation (RPA)
Robotic
Process Automation (RPA) is one of the closest successful solutions in helping
companies convert their data from papers to structured digital formats. RPA
relies on the rules that the documents might follow. The papers are scanned and
processed as an image in the RPA software. The software tries to identify the
set of parameters on the image, which need to be translated into the structured
database. The set of parameters is searched based on certain rules of the
document, which could be the sequence of pages, the sequence of words on the
pages, or some other form of a landmark for identifying the parameters on the
paper. This approach mostly fails if the paper documents do not follow any
template, or there exist multiple pages with similar contents, or if most of
the contents on the paper are handwritten. Considering the dynamic nature of
the documents, it becomes difficult for the software to define rules based on
which the process can be completely automated.
Optical
Character Recognition (OCR)
Many
solutions/software rely on Optical Character Recognition (OCR) engines as one
of the primary components in their toolbox, which is further combined with
techniques from the Natural Language Processing (NLP) domain, to try to make
sense of the extracted texts. But many solutions fail due to the inability of
the OCR to provide accurate results on scanned pages containing hand-written
texts, special symbols, marking, notes, etc. This leads to breaking the flow of
a possible fully automated solution.
The
following sections talk about various possible OCR engines from the leading
firms in the market and try to explore and evaluate the performances of the
OCRs specifically on hand-written texts. Further, an experiment is performed to
try to integrate the OCRs with custom-built software that can use the OCR
output and try to structure the data from scanned pages into a database. The
pros and cons of this approach are explained in subsequent sections.
Furthermore, to overcome the shortfalls of OCR technology, an alternate
approach is suggested, using Speech-to-text for data extraction.
Speech-to-text
The
speech-to-text approach is also integrated within a custom-built software to
evaluate the efficiency of data extraction, in terms of speed and accuracy. The
speech-to-text based solution is further investigated on its scalability
aspect, and how much time and effort would be needed per batch records are
calculated.
