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Optical Character Recognition (OCR) is an electronic or mechanical conversion that automates data extraction from scanned documents, photographs of texts, and image-only PDFs. Machines don't understand text in images like they do with text documents or how humans read text from pictures. However, with OCR technology, the system can recognize text in images and convert them into machine-readable text documents which can also be edited and analyzed by other softwares.
The scanner converts documents into binary data, with light areas classified as background and dark areas as text or vice versa.
The OCR software cleans the image by deskewing, despeckling, and cleaning up boxes and lines.
OCR algorithms, pattern matching, and feature extraction are used. Pattern matching compares character images (glyphs) with stored ones of similar font and scale, suitable for known font documents. Feature extraction, on the other hand, breaks down glyphs into features to find the best match.
The system converts the recognized text data into a digital file.
Instead of spending 10 minutes manually typing in the data, we can simply scan and review the result. Furthermore, it accelerates the verification process, completing it within just a few minutes.
By automating document routing and processing, it boosts operational efficiency. For example, manual data entry for hand-filled forms becomes unnecessary, document retrieval becomes quick through keyword searches, and handwritten notes can be converted into editable text.
By facilitating artificial intelligence, we can make the tasks work automatically and reduce the need for manual intervention, and thus also cut down our labor costs.
Generative Pre-trained Transformers (GPT) models are general-purpose language models capable of handling various tasks such as creating original content, writing code, summarizing text, and extracting data from documents. GPT belongs to a family of neural network models that uses the transformer architecture. Neural networks are a type of machine learning algorithm that mimic the structure of the human brain. In GPT, the neural network serves to connect the input and output text data. GPT also employs the transformer architecture, a new Natural Language Processing (NLP) model, which uses an attention mechanism memory system to process long sequences of text effectively.
GPT’s main capability is its ability in comprehending the structure and meaning of natural language text. To learn a wide range of patterns and relationships in the text, GPT undergoes a training on a large and diverse dataset of text. For the training, NLP techniques such as part-of-speech tagging, syntactic parsing, and semantic analysis are utilized. These techniques allow GPT to grasp the grammatical structure, identify the word classes, and understand the meaning of the sentence.
The training process of GPT involves a combination of both supervised and unsupervised learning. During supervised learning, the model is provided with labeled data, and the desired output for each input is specified. This training type is similar to how a child learns when a teacher explains the meaning of a word. In GPT’s case, this involves training the model on a vast amount of text data, where the inputs are sentences or paragraphs and the outputs are predictions of the subsequent words.
On the other hand, unsupervised learning refers to an approach where the model learns to identify patterns and features in the data without explicit labels, similar to how a child learns by observing and exploring the world. For GPT, this entails training the model on a massive amount of text data without any specific labels or outputs. Through this unsupervised learning, the model gains an understanding of the patterns, relationships, and meaning for words, phrases, and sentences. Once GPT completes its pre-training phase, it can be fine-tuned on a smaller, task-specific dataset, which allows it to perform a specific NLP task.
GLAIR OCRGPT was developed to accelerate the development process of OCR models. Utilizing the GPT-4 model, it significantly shortens the development time from the usual 4-8 weeks required for custom-built models to as little as 2 weeks. In addition, it also enables the new model to support any types of documents.
First, the advantages of GLAIR OCRGPT stem from the fact that the GPT model is already pre-trained. This means GLAIR OCRGPT needs fewer training samples compared to traditional or custom-built OCR models.
Second, GLAIR utilizes the GPT model to enhance the OCR system’s text processing capabilities. Being a language model, GPT excels in understanding context, inferring word and sentence relationships, and recognizing keywords. Moreover, it can summarize texts and create structured formats. This integration facilitates OCR to swiftly identify and organize important categories or fields from the extracted text into a user-friendly and easy-to-process outcome.
