What is OCR?

Machines can’t understand text in images like how us humans do. However, they can understand text within text documents. Therefore, in order for machines to understand text in images, we have to use a system that can convert the text in images into machine-readable text documents. That system is what we call Optical Character Recognition or OCR. OCR helps in automating text extraction from scanned image files or photographs and converting the recognized text data into a digital file. 

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Do Data Volume Affect OCR?

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The answer is “Of course!” For optimal text recognition and to create an organized extracted text, OCR requires a lot of resource data. This is because OCR runs on templates and rules to perform, and it is hard to cope with diverse formatting or unstructured documents. 

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For example, OCR will struggle to do a packaging’s text extraction because:

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• First, there are various fields, styles, and formats of packaging. Each new field, style, and format needs their own rule, and adding more rules would mean more data and resources are needed.
• Second, there is not much demand from users. This, in turn, also means there already isn't enough resource data to make the “templates” and “rules” in the first place.

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So, yes, of course the amount of data an OCR system has highly affects its capability in producing accurate and organized extracted text.

What is GPT?

Generative Pre-trained Transformers (GPT) models are general-purpose language models. This means that GPT is capable of handling various tasks related to text and language such as understanding, analyzing, summarizing, translating, and even producing coherent text. GPT’s main capability that people speak of so often is its ability in comprehending the structure and meaning of natural language text.

GPT is able to grasp grammatical structures, identify word classes, and not only lexically but also semantically understand the meaning behind a phrase, sentence, or paragraph. To learn the wide range of patterns and relationships in the text, GPT undergoes a supervised or unsupervised training on a large and diverse dataset of text. For the training, Natural Language Processing (NLP) techniques such as part-of-speech tagging, syntactic parsing, and semantic analysis are utilized.

How Can GPT Push OCR Implementation?

Normally, the output of OCR is already highly accurate if the documents are simple and come with few variations. However, nowadays many businesses have found instances in which they need to process other types of documents that might have a large amount of variations and might not have an adequate user demand from other people. And this is where GPT becomes handy.

1. Adaptability and Flexibility: GPT, which has been pre-trained on a large dataset, enables it to adapt well to various tasks, even in scenarios where it has limited training data. GPT is capable of normalizing and interpreting variations in writing styles, structures, and formats. In cases where OCR is used for documents that have unique formats or have a small resource data, GPT's broader linguistic knowledge can compensate for the lack of training in that specific area.
2. Data Augmentation: By using GPT to generate synthetic data or augment your existing dataset, the OCR model can be trained with a more diverse set of examples, even if the actual user-generated data is limited. The idea of data augmentation is to artificially increase the amount of training data available for a given task by automatically creating new training examples. Recent advancements in generative models such as ChatGPT make it possible to achieve realistic but unique data for the augmentation process. Data that is augmented with synthetic samples is proven to still yield a good performance and particularly aids in low-resource settings.
3. Contextual Understanding: GPT, being a language model, excels at understanding context and can provide a deeper understanding of the extracted text from images or documents. This can be particularly helpful when dealing with ambiguous or complex content, especially when there are only a few users, as GPT models can adapt to their specific word usage, format, or style. Being able to understand also means that GPT can provide additional information to enhance the accuracy of interpreting the text based on their fields.
4. User Assistance: Lastly, GPT can also be integrated into OCR to provide user assistance. For example, if OCR results are uncertain or incomplete, GPT can be used as a virtual assistant to ask clarifying questions to users and obtain more accurate information.

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In conclusion, GPT's advanced language understanding capabilities and large dataset training can complement OCR's ability to extract textual information from images. This collaboration enhances not only the accuracy of text extraction but also provides contextual understanding and user assistance, making the technology more adaptable to varied documents. Those benefits are exactly what GLAIR Paperless with OCR can offer you!

GLAIR Paperless with OCR doesn’t need a large amount of data to be able to give you satisfactory results, and naturally that means you can use it for various types of documents.

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