The global OCR market is expected to be valued at US$ 51,527.0 million by 2030, and “to expand at a
CAGR of 15.2% during the forecast period from 2020 to 2030,” according to a report published by
Transparency Market Research, in April 2020.
Optical Character Recognition is a popular and revolutionary technique used to recognize text from
images and convert it into machine-readable text data. The traditional method of keeping track of
data by using hand-held, printed or handwritten documents, comes with its fair set of
challenges/drawbacks such as the need to manually sift through pages to derive required information,
difficultly in allocating storage space, and the amount of resources (paper, ink) used in the
process. With OCR, documents, across languages, can be digitized, and saved on the cloud, thereby,
enabling easy access. Furthermore, the digitized documents can now be used to derive insights and
make existing analyses richer.
Through this article, we will take a look at how the use of OCR has evolved over the years, and
exciting future use cases around the corner.
A Brief Timeline of OCR Technology
1910s – Optophone was built - an electronic device that scans the printed
presents sound combinations enabling the visually-impaired to read.
1950s – Gismo was developed - a device that could recognize the 26 letters
of the alphabet
used in a standard typewriter.
1970s – Flatbed scanners were built to help digitize printed text – this
device could only
read certain fonts designed for machine readability, during its initial inception.
1990s – Newton Message Pads were enabled with handwriting recognition -
while it was not a
commercial success, it was a pivotal turning point for OCR technologies.
2000s – Google took up the OCR software, Tesseract, and in a few years, it
was mapped to
neural networks to create, arguably, the most popular OCR engine.
Use Cases of OCR technology
Digitization, automation of data: With OCR, manual data entries can be
automated. Automating the process reduces the number of human errors, while digitization
increases ease of data access, improves data security, improves cost efficiency, saves
space, and is also more environment-friendly because it cuts down on paper usage, use of
Document verification: This technology can be used to verify important
documents such as
passports, VISAs, and other proof of identity.
Automated vehicle number plate recognition: By using images captured
with CCTV cameras,
miscreants can be reprimanded, and traffic rules can be enforced efficiently.
Assist visually-impaired people: OCR technology can read printed text
aloud and thereby
assist the visually-impaired.
Language translation: OCR can help translate content from one language
to another – a
technique that would prove useful while travelling/meeting people from around the world.
Evidence Management: Managing and sifting through copious amounts of
evidence is a
pivotal part of investigative process for defence/police authorities. Setting up OCR
technologies can help to quickly access relevant evidence and create image-word
associations, improving searchability of evidence.
Managing Auditing: Bank and credit card statements can be analyzed with
accuracy using OCR technology and help in detecting fraudulent transactions, anomalies,
or aberrations by sifting through digital databases – significantly faster than having
to physically go through piles of paper. This will also help bring down auditory
Live Casinos: OCR technologies form a pivotal part of online casinos
and help recreate
the climate of brick and mortar casinos, and collect pertinent data on the game, from
the way the die is rolled to players’ performance, etc.
EdTech: In the wake of the COVID-19 pandemic, as many students are
studying from home,
OCR technology is being leveraged by EdTech firms to help guide students as they study
online. An APAC-based online tutoring platform, for instance, is empowering the “doubt
solving segment of online learning,” by utilising OCR technology and AI to extract text
from the photos shared by students, and matching it with the accurate answer from its
Every image use case is different, and a single code would not suffice to decode it. However, adding
relevant parameters when coding and tweaking them as and when needed, can help extract text across
different types and forms. Here are three broad, yet simple steps that OCR typically entails:
OCR generated text could result in typos or other errors, when posed with illegible analog text,
non-text symbols, formatting incongruencies, blurred text, unusual font, etc. However, computer
vision and machine learning algorithms can help avoid these errors to a large extent, and OCR tech
can also be setup with automatic correctors to identify errors.
What does the future hold for OCR?
While OCR technology has evolved over the years, even to this day, “extracting text from an
image,” is easier said than done. Absence of metrics is one of the primary challenges to
leveraging OCR effectively. Much like an NLP exercise, samples need to be visually scoured
through to determine satisfactory outcomes, and words and numbers need to be manually
checked in few images to gauge the accuracy. However, building a deep neural net with plenty
of samples can help train the models on factors such as font style and document layouts.
Converting handwritten text to digital text is another instance that is yet to be perfected
and still in exploratory stages – while numbers are accurately determined in most cases,
alphabets are relatively more challenging, given the different font and writing styles.
Leveraging neural networks and Capsule Networks can help mitigate the challenges and
increase ease of working with handwritten texts. In fact, with deep learning, text can
reportedly be recognised with approximately 99.73% accuracy.
A report published by Transparency Market Research noted that North America was expected to
be a major player in the OCR market given the change in government policies and regulations,
as well as infrastructure development, while the APAC region is expected to have the highest
CAGR in the forecast period (likely driven by small & medium-sized enterprises, adoption of
tech by the IT & Telecom industry for document management), and the overall growth of the
market is expected to be fuelled by the increasing demand for software in the Middle East &
Furthermore, use cases are also growing far and beyond the traditional analog to digital
text conversion. For instance, OCR is being utilised in researching historical texts. The
University of Connecticut, for instance, shared that “The UConn Library and the School of
Engineering are working to develop new technology that applies machine learning to
handwriting text recognition that will allow researchers to have improved access to
handwritten historic documents.” Machine learning is being applied to handwriting text
recognition, and the characters identified are used to create algorithms that help to
recognize patterns, and leverages them to form neural networks and systematically learns
from them, akin to the processes of a human brain.
Many business leaders are also shifting their focus beyond machine learning to deep
learning, because “Driven by deep learning, [OCR] is entering a new phase
where it first
recognizes scanned text, then makes meaning of it. The competitive edge will be given to the
software that provides the most powerful information extraction and highest-quality
OCR technology has evolved over the years, albeit intermittently, and its usage is only
expected to grow by leaps and bounds in the future. By leveraging the needed AI tools and
techniques, we can move beyond the traditional, intended use cases. And with AI-powered OCR,
pictures will not just speak a thousand words, but also be able to derive thousands of
meaningful insights, devoid of human error.
Author - Tanvi PS | Analyst | TheMathCompany
On any given day, Tanvi is likely to be thinking about when she gets to eat pasta and mashed
potatoes next. In her downtime, she can be found collecting unique postcards at bookstores,
jamming to music, re-watching series/movies, or pondering over human existence.