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.
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.
For example, OCR will struggle to do a packaging’s text extraction because:
So, yes, of course the amount of data an OCR system has highly affects its capability in producing accurate and organized extracted text.
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.
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.
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.