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

Since the 1990s, Optical Character Recognition (OCR) has been widely used. Enterprises utilize OCR to scan documents like invoices to create digital copies and manage physical documents.

Traditional OCR platforms can convert handwritten or printed text into machine-encoded text and store them as data. Receipts, bank statements, passports, or other documents can be processed through an image-to-text converter. A popular application of OCR is Adobe Acrobat’s PDF Editor.

Until today, the OCR market is still growing strong. According to a report by Grand View Research, the global OCR market size will be worth $26.31 billion by 2028, 3.5x of that in 2020. Enterprises are investing in technologies that help them digitize their processes and increase productivity.

Integrating Artificial Intelligence (AI), specifically Machine Learning and Deep Learning, helps companies process documents and data more efficiently. These technologies also improve the accuracy of OCR. This will reduce the cost of document processing and offer many more insights, multi-language translation, etc. rather than just a digital way to store physical documents. 

Traditional OCR vs. AI-driven OCR

Traditional OCR

A traditional OCR converts printed text to data, automatically extracting invoice data using templates. These templates usually have fixed page locations for each data field or an if-then rule to tell the software to find specific information.

The setup process is usually long and expensive as each alteration requires new rules. Not to mention the low accuracy rate due to zero flexibility while processing a variety of documents. Especially when it comes to documents like invoices, they have very high variability.

Here’s an example of the same rules applied to different invoices, causing failures in traditional data capture.

Source: Rossum

Several difficulties with traditional OCR includes:

  • Image quality
  • False Positives
  • Text overlap
  • Tabular data
  • Errors in document classification

AI-driven OCR

Meanwhile, an AI-driven OCR can detect contextual information and interpret patterns and features in different document variations and types with Natural Language Processing (NLP). Handwriting can also be converted into data with the help of Machine Learning.

The goal of AI development is to imitate how human brains behave. So instead of having staff manually check the data captured by traditional OCR. AI-driven OCR’s goal is to capture, process, and streamline data accurately into the system.

AI takes into account the available data and finds connections as well as correlations between data structures. Gradually, it creates a pool of knowledge that adapts over time, making the algorithm more mature and accurate.

At the same time, difficulties with traditional OCR can be solved with an extensive database to train the AI. The power of an AI lies within the database behind it. The more resources are trained on the AI, the more mature it can be.


Traditional OCRAI-driven OCR
Set upRequires manual efforts for templates settingsMachine Learning structures extract data and insights from complex sources
MaintenanceRequires regular maintenance, rules & templates updates by expensive expertsMaintained continuously by learning AI
ValidationRequires human validationAutomated validation based on existing database
AdaptabilityCan only extract data from structured documentsCan extract data from unstructured documents and images
AutomationUp to 50% of tasksUp to 98% of tasks
Comparison between traditional OCR and AI-driven OCR

How does AI-driven OCR work?

AI is the game-changer for OCR in three main tasks: classification, extraction, and validation.


Classification, a.k.a. document sorting, is the process of distinguishing between checks, invoices, orders, and other forms of documents. The AI-driven OCR can automatically classify documents based on their contextual information.


AI can extract data from both semi-structured and unstructured documents, including handwritten information. Even with invoice number identification, a complex task, AI can train itself to understand the context (what is not an invoice number and what should/shouldn’t be around the number). Hence the higher accuracy.

Mature AI can easily extract complex tables with lines that don’t match up. It learns how to understand patterns and formatting, differentiates types of information, and identifies key data elements.


Provided with an extensive database and integration into other systems, AI can validate the extracted data and ensure its legitimacy.

AI-driven OCR allows multi-way search, which means using multiple fields to match an exact item in the back-end system. Even if an abbreviation is used in the invoice and doesn’t match with the database, the AI can still deduce if they are the same item.

Here’s an example of how the GEM AI-driven OCR Engine captures data from a tax invoice.

How GEM AI-driven OCR Engine captures data from a tax invoice
How GEM’s AI-driven OCR Engine captures data from a tax invoice

Benefits of AI-driven OCR

Detect multiple languages with high accuracy 

The most common use of OCR is for transforming printed 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, the capability to detect, match, and recreate digital versions from physical papers is still weak. It is because the former languages have a simpler set of spelling rules.

Chinese and Arabic are two of the five major languages. 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. 

With AI friendship, 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 across several languages, which further enhances the translation ability. The most prominent example 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 of Chinese characters comes 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 a short processing time. They tested on four neutral networks, all of which produced 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 difficulty performing this task precisely. These remain challenges and potential in the investigation, information security, and customer engagement. 

Therefore, many attempts have been made to tackle this untouched 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 accurately detects text in images and videos. In stage 3, Convolutional Recurrent Neural Network (CRNN) is resorted to recognize texts.

Gain new insights and productivity improvements 

Traditional OCR can only produce digitized texts, but AI’s assistance can be much more.

Deep learning assists ORC systems in memorizing texts as well as the meaning and making new sense by itself, which helps businesses turn data into digital insights. For example, an insurance firm that converts contracts to an electronic format will only have a limited gain. However, if the business can analyze contracts and 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 these 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 its appropriate deployment to gain competitive advantages.

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