OCR Solutions and AI Techniques For Intelligent Data Extraction | Hacker Noon

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@emily-danielEmily Daniel

Emily is a tech writer, with expertise in entrepreneurship, & innovative technology algorithms.

In this fast-paced world, businesses and organisations are in a race to get ahead. In this competitive streak, the most tedious task that holds back the progress is data entry operations. Although it is a significant responsibility, entering data from their hard forms to online forms takes a lot of time. Even after a large management staff manually feeds the data into the systems, there is still no guarantee on the accuracy of the data entry process. No matter what, the human eye is always at risk to make errors. 

Gartner’s report states that the revenue spent by businesses on paper consumption is 3%. The waste of composed paper consists of 50% of their waste.  On top of this, the unit 4 study states that on average, time spent by office workers on administration tasks is 69 days annually. This also results in a loss of $5 trillion in annual productivity.  

OCR (Optical Character Recognition) is the ultimate answer to this plight. Optical Character Recognition is a technological solution for data entry operation. Many businesses have benefited from this automated optical character recognition solution. Let us talk in this blog about how OCR combined artificial intelligence has data extraction more accurate and seamless. 

What is OCR technology? 

OCR technology extracts the data from its image or document and turns it into text form. OCR technology can scan documents like ID cards, driver’s licenses, utility bills, receipts, invoices, passports, contracts, etc. These documents can be extracted from their handwritten or printed physical form and turned into the machine-readable message so that the system can understand it and display it in the online form. This way the workers will not have to spend hours typing in every document online.

A traditional OCR system works by detecting the patterns, it analyzes the image. If there is any text on the image, then it extracts that text into a format that can be read by a machine. The scanned document turns into a digital editable format. 

However, this OCR technology is just limited to extracting data and turning it into digital form. There is still no guarantee on how accurately and error-free this task is completed. The issue still remains as the labour is still used in correcting the errors and time is yet again wasted. The demands of the businesses have out-turned its growth. This is why a more intelligent solution to data extraction is of the essence.

Artificial Intelligence to Rescue OCR

The OCR solution is built on artificial intelligence, uses a machine-learning algorithm for data extraction operations. It uses a computer vision and language processing algorithm to extract the text in its image form and provide more precise results to the end-user. With the help of AI-based technology, the OCR can understand the languages, type of the document, context, format, and other minor details related to the document. AI-based OCR has a detailed understanding of the data within a document. It guarantees an accuracy rate of ninety-nine percent. The AI-based OCR engine eradicates the requirement of human help to make any edits.

AI-based OCR has three steps to it:

Preprocessing

In order to have full character recognition, the preprocessing of the images occur using various techniques.

  • De-Skew and Despeckle. To give the document a perfect alignment, the De-skew technique is used. This extracts the data accurately without any spots or crumbled up pages, and aligns the data properly. This process also smoothens the edges of the document along with removing the spots. 
  • Binarisation. A binary image is a grey-scale image i.e. black and white image. Binarisation occurs when colored images are turned into a binary image. As most OCR software works on the binary image, this process is quite necessary. This also influences the quality of recognition. 
  • Layout and Line Removal Analysis. This technique is used to identify the columns, paragraphs, etc. It filters out the non-glyphs boxes and lines. With the help of this, the data excretion process is done thoroughly as data written in column form can be identified. 
  • Script Recognition. The identification of the script is quite necessary before the data character recognition process as a script can be altered through the level of words, especially while dealing with multilingual documents. This process helps to better the data extraction. 

Character Recognition

Pattern recognition and Feature Extraction are two ways through which character recognition can be carried out. Pattern recognition uses the matrix Matching algorithm. With this, the image is compared to the stored glyph. Pattern recognition can be used for typewritten documents that have the same font.  However, pattern recognition can be a bit unclear when it comes to dealing with multilingual documents. Feature extraction does not identify the character as a whole, it identifies the particular character components individually by decomposing it into features. 

Automated Form Population

This is an automated process of data entry. The stored data in the memory is populated in verification fields which saves time for the end-user. OCR engines can be enhanced with post-processing techniques. These techniques include near neighbour analysis which corrects the errors and highlights the words that should have been written together. 

The AI-based OCR engine has decreased the burden of many businesses. Now their data entry process can be carried out smoothly with no time wasted on long and boring process.

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Emily is a tech writer, with expertise in entrepreneurship, & innovative technology algorithms.

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