When it comes to automating the document processing workflow, it’s impossible to avoid the question of Artificial Intelligence or AI. It’s difficult to do anything nowadays without some use of AI-powered tools. Recent studies have shown that at least 40% of the average workday will soon be done by AI.

Does it mean that OCR won’t be needed, since AI will be able to do the same job? Here, we show that the answer to the question “do you need AI for OCR?” is  “No, not really.”

How is AI used in OCR applications?

Any OCR software consists of two parts. An OCR engine that reads an image and turns it into machine-readable data; and a document processing function that helps to deal with all this data. Do you just need to grab an invoice number and use it as a file name? Does information like customer name or account number need to be saved in your database? The processing function allows the software to DO something with the information on the page, it goes beyond simply turning a picture of text into actual text. While many programs under the heading of “OCR” use the same engine, what sets them apart from one another is if and how they can use the words and make them into data.

For the last 10-15 years, most of the OCR engine technologies have been built on AI training.

OCR training algorithms use AI to improve recognition accuracy and automatically identify common data elements based on learned context. It is tuning the OCR engine to improve recognition of new fonts, languages, or handwritten text.

Training of OCR engines with AI is an important process that enables artificial intelligence models to efficiently and correctly extract data from scanned documents, having many practical applications in a broad range of business fields. Recent advances in AI allow OCR systems to perform at higher levels of accuracy and efficiency by collecting large amounts of data from scanned documents and using it to identify patterns, characters, words, and other elements of text. The more data, the better the performance and accuracy.

It would be difficult to find an OCR engine that was not trained with AI, unless you are ready to dig deep into archives. All current powerful OCR engines are trained with AI: Tesseract, AWS Textract, FineReader and many others.

How do you train AI for OCR data capture?

The second part of AI OCR training refers to training data capture software to identify the correct location of fields on various related documents.

OCR training is used by enterprise data capture applications to automate the creation of recognition templates. The most common application is accounts payable invoices, where every vendor has their own layout and formatting but shares the same data fields. These systems “learn” from user feedback, improving the recognition precision and consistency.

Field position training generally starts with a generic template that can identify the fields using the most common labels. Whenever a field is missed or read in the wrong position, the user highlights the correct field position on that document during a manual review. The new position is recognized by the machine learning algorithm, which generates an updated template that correctly identifies the fields on that sample. When the document has a consistent layout and decent image quality, the template can be trained after just 2-3 samples.

Complex documents with more layout variation can take many samples to train, or in some instances, fail to train altogether. AI OCR training is not magic, and there will always be some cases where it is unable to consistently read a document correctly. If 100% accuracy is needed for these documents, then it is important to choose a data capture platform that offers the ability to manually override the OCR training.

Many newer OCR systems no longer offer the ability to manually create templates and rely fully on the machine learning function. While these systems can be easier to configure, they will never reach the level of accuracy that can be achieved by one that offers a manual override.

Do you need LLMs for OCR?

Large Language Models (LLMs) like OpenAI, Gemini, or DeepSeek process images in a different way. As models that were created to handle text, they take a multi-step process to do that.

1. Image Encoding. Initially these models convert the image into a set of data points with the use of Convolutional Neural Networks (CNNs). CNN is a specific type of neural network that helps scan the image and pick out patterns like edges, textures, and shapes, condensing them into a simpler, encoded version.
2. Feature Extraction. These data points are then used to find the more important parts of the image. The model examines different features, such as objects, faces, or text within the image. Advanced attention mechanisms help the model focus on the most relevant parts, like skimming a page for the key points.
3. Creating Tokens with Cross-Modality Learning (CML). CML is a method that connects found features to a text. LLMs are trained on millions of pictures with captions and allowing them to associate visual data with information. The whole image can then be viewed as a sequence of tokens.

More modern models of LLMs use Vision Transformers (ViT) instead of CNNs for image recognition. Basically, it is a similar process, but rather than simple basic elements ViTs are trying to figure out large themes and patterns to assemble a more holistic, total understanding of an image.

In general LLMs can process images rather well and fast, however they do heavily rely on the number of images they were trained on. It is also unclear for LLM human users what was the internal logic of the process. LLMs also require giant datacenters with specialized servers, where traditional OCR can be run on any processor. The cost to process a single image is obviously much higher as a result.

When should you use LLMs for OCR?

Since all OCR applications use “Artificial Intelligence” algorithms to varying degrees, and Large Language Models like ChatGPT, Bard, Grok, or even DeepSeek require significantly more overhead to process an image, the question becomes “when should you use LLMs for OCR?”

The answer really depends on what kind of documents you are working with. This guide will help you understand the types of AI used in OCR, when to use AI for OCR applications, and when it’s practical to deploy LLMs for data capture applications.