language technology

OCR with AI-based features is a promising tool to unlock multiple languages’ accessibility, imagery content, and work efficiency.

OCR (Optical character recognition) has become a human trusted assistant for text scanning and translation with increasing accuracy. Surprisingly, it originated 100 years ago but had not experienced much innovation in the mechanism until recent years. 

When paired with AI, specifically Machine Learning and Deep Learning, OCR can offer much more insights, multi-language translation with incredibly high preciseness, and productivity solutions rather than just a digital way to store physical documents. 

This article will give further explanations for each of the new implications of AI-based OCR.

Detect multiple languages with high accuracy 

The most common use of OCR is transforming print documents into readable and searchable data for computers.

Optical character recognition functions well with English or Roman languages (e.g., French, Portugal, and Italian). However, in other systems, such as logograms or syllabaries, its capability to detect, match and recreate digital versions from the physical papers are still weak. It is because former languages have a simpler set of rules of spelling.

Chinese and Arabic are two of the five major languages, according to Statista. The words are formed by various characters with various meanings, making it challenging for OCR to identify and replicate, meaning there are possible values that OCR can contribute. 

phone picture of text

With AI befriends, current advanced OCR can deal with this issue. With Deep Learning, the OCR programs can detect and understand more complicated characters from logograms, syllabaries, and other scripts. It can also learn to match words among several languages, which further enhances the translation ability. The most prominent case of this implication is Tesseract, the OCR system developed by Google, which detects texts in 100 languages, including right-to-left languages like Arabic and Hebrew.

Another specific example for Chinese characters is from experts of the Institute of Electrical and Electronics Engineers (IEEE). They have successfully developed Deep Learning-Aided OCR Techniques that can recognize Chinese uppercases with great accuracy and short processing time. They tested on four neutral networks, all producing highly accurate results:

  • convolution neural network
  • visual geometry group
  • residual network
  • capsule network

The highest outcome was that 99,38% of texts were detected correctly.

Identify unstructured text 

Another use of OCR technology is to detect and transfer texts from images, i.e., texts that are hand-written or captured in photos with complex backgrounds, fonts, lighting, and geometrical distortions. Nevertheless, conventional OCR programs have difficulties doing this task precisely. These remain challenges and also potential in the investigation, information security, and customer engagement. 

OCR technology

Therefore, many attempts have been made to tackle this un-touch land. Technology firms try to deploy deep learning-based OCR to transform unstructured texts by creating a system that includes three stages: 

  • image processing
  • text detection
  • text recognition

In stage 2, they use a deep learning method called EAST: An Efficient and Accurate Scene Text Detector. Experts from Cornell University claimed that this method detects texts in images and videos with great accuracy. In stage 3, Convolutional Recurrent Neural Network (CRNN) is resorted to recognizing texts.

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Gain new insights and productivity improvements 

Traditional OCR can only produce digitized texts, but the assists of AI can be so much more.

Deep learning assists ORC systems in memorizing texts and meaning and making new sense by itself, helping businesses turn data into digital insights. For example, an insurance firm that converts contracts to an electronic format will only have limited gain. However, if the business can analyze contracts and analyze their risk exposure, there will be many more valuable benefits.

Deep-learning-based OCR software can generate productivity, too. AI-based ORC programs can scan and copy mortgage documents, while AI helps to determine high-priority loans. The software reduces conventional progress from hours to minutes. 

In short, combining AI and OCR is proving a winning strategy for both data capture and management.

With the promising implications, it is reasonable for business owners in these sectors or any business that involves the OCR method to closely keep track of its new developments and consider appropriate deployment to gain competitive advantages.


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