Handwritten documents typed documents, and digital images can be converted into readable text for machines, using Optical Character Recognition (OCR). OCR is very useful nowadays in many industries to convert a huge number of printed or paper documents into digital text files.
OCR system consists of two parts, hardware, and software. The Hardware part can be an OCR scanner that can be able to convert paper documents or images into digital form. On the other hand, the software part is the algorithm itself.
How do the OCR Recognition Algorithm works?
Humans are able to understand and recognize different types of patterns, styles, fonts, and handwritten documents, but it is tough for computers to do so. Computers are able to read graphics. Scanned documents are converted into pixels, and computers recognize and convert them into editable text files.
Here are the steps that complete an OCR technology algorithm:
- Image Acquisition
First of all, is to require images using an OCR scanner of paper documents. In that way, captured images can be stored. Normally, paper documents are black & white tone. OCR scanner must be able to convert images from colour to binary images.
- Pre-processing
This process is used to make the input file/image usable by the OCR recognition algorithm. It removes noise from the image and unwanted background automatically. This phase splits into two parts:
- Layout Analysis: it analyzes the whole image and identifies different types such as captions, graphs, and blocks.
- De-Skew: if the document wasn’t properly aligned while scanning, it tilt documents on it to make lines in symmetry.
Pre-processing allows you to get clear images to yield better results.
- Feature Extraction
Some algorithms take advantage of curved and crossed character properties. to identify characters in input files. For example, the letter “E” is identified as one vertical line and three crossing horizontal lines. OCR recognition algorithm hosted by Neural Network (NN) uses various logic, where the first layer of NN, accumulates pixels from the input file to create a low-level map of the image.
- Post-Processing
The process of image refining is being done at this phase, as the OCR model may require some corrections. However, achieving 100% accurate recognition results is not possible. Character identification depends on the context massively. Human-in-the-loop approach is required for the verification of the output.
How OCR Recognition Algorithm help startups?
Here is what OCR recognition algorithm offer to your startup:
Cut Down Costs: It converts files into digital format and automatically fills the data accordingly, which reduces your time.
Increase Customer Satisfaction: This technology allows people to update their personal or any other information from any place remotely instead of visiting offices physically, by scanning identity documents.
Offer cheaper backup options: Using this we don’t have to store paper documents along with their duplicates, which consumes massive physical storage.
Translate documents: Some OCR tools can translate documents from one language to another, which is quite useful for organizations spreading worldwide.
What are Optical Character Recognition (OCR) algorithms Top Use Cases
There are several industries, that are using OCR applications, some of them are described below:
Invoice Imaging
invoice imaging is being used widely in different industry applications; it helps keep track of financial records and prevent payment backlogs. It helps to simplify data collections compared to other procedures.
Banking Sector
One of the major applications of OCR in the banking sector is that it is being used to process cheques without human resources. A cheque is drawn into the machine, that verifies different details to transfer funds.
Handwritten Recognition
It allows computers to recognize handwritten input from sources such as paper documents, and photographs and interpret it intelligibly. Image of handwritten text can be identified from a paper by the OCR recognition algorithm.
How Opting for OCR can be Challenging for your Business:
Using OCR for your business, you have used these cautions;
Input Material:
Your input files must be appropriate for the OCR. For example, files must not be damaged, pages must be aligned, and more.
Handwritten Files:
As handwriting varies from person to person, in some cases a person writes in different styles. It might possible that some styles must not be in the datasets.
On a Final Note:
Using the OCR recognition algorithm makes it easy to maintain documents in-house for any organization, it gives a start to automate workflow, and it eliminates the need for paperwork. High-level OCR might be helpful for mid and large-sized organizations to make a profit using custom-tailored algorithms. OCR recognition algorithm can be proved more beneficial for the industries such as healthcare, finance, and tourism.
Additionally, businesses can propose a modified method of back-propagation. Neural networks use this extensively. As a result of the proposed method, the error rate of neural networks is computed more efficiently, increasing their accuracy.
