Document AI reduced/replaces the need for humans in converting documents into digital format. It used Natural Language Processing (NLP) and Machine Learning (ML) in training and to get knowledge. Once trained, it can process various types of information contained within a document.
Actually, the document's variety of formats makes it quite a challenging task to process documents. In the document, there are several layouts, such as images, tables, barcodes, handwritten text and logos. Processing becomes tough due to the variation and differences in every layout. Apart from this, the quality of document images might affect the procedure of processing.
Today, data expands at a higher rate. It is approximated that by 2023, unstructured data makes up over 80% of enterprise data. Organizations predict generating 73,000 exabytes of data in the year 2023 alone.
By 2028, about 70% of data will be estimated to be stored in unstructured format. This up trend will be an enforcement of necessity for machine learning AI solutions to address.
Accessibility can become the greatest barrier to the wider adoption of Document AI very quickly. While all Amazon AWS, Google and Microsoft Azure offer powerful Document AI tools backed by their cloud services, the costs can runaway rapidly. Most often charges are levied on per-page basis or per thousand characters handled.
This may pose a cost barrier for smaller businesses or individual practitioners into ordering advanced document processing technologies if their user-levels are low but the amount of processing is high. In the following sections, we take a look at state-of-the-art models that allow us to build custom Document AI pipelines.
Document AI leverages Machine Learning (ML) and Natural Language Processing (NLP) to extract actionable information from free-form documents.
I'll explain the process in steps:
Ingest: The first step is to ingest the PDF. This can be done manually by uploading the PDF to the Document AI system
Once the PDF has been ingested, it is preprocessed so as to prepare the document for analysis. This may be inclusive of working like image quality detection and noise removal but using powerful multimodal models even noisy data can be tolerated to a certain extent.
Some systems would then try to improve the quality of the image or maybe de-skew for better performance.
Document Layout Analysis (DLA): DLA is performed to understand the structure of the document, which includes detecting and categorizing text blocks, images, tables, and other layout elements.
Optical Character Recognition (OCR): After DLA, OCR is applied to the structured layout to accurately recognize and convert the text within each identified block into machine-readable text.
Extraction: The system would then go ahead to extract information pertaining to the entities and relationships since it would have a structured layout and recognized text.
For instance, a multimodal model such as a Transformer trained on a large-scale dataset of documents may directly accept text and visual features in place of traditional OCR. In addition, fine-tuning multimodal models can be done to learn certain layouts and data types within documents.
Analysis: So currently the Document AI system does analysis of textual and visual information and interprets the content. It evaluates sentiment, discerns intent mapping relationships that exist between entities not to mention classifying documents by type. This might include sophisticated operations like semantic analysis, understanding of the context, and applying domain-specific rules for content review.
Output: The extracted information is then output in a format that can be used by downstream applications, such as data analytics tools, customer relationship management (CRM) systems, or other enterprise software.
Moving or the transition made to the newer models like RNNs, CNNs, and majorly Transformers can be evidenced from the fact of the ever-evolving nature within Document AI. RNNs find a particular application for sequential data. CNNs find applications in spatial pattern recognition capabilities.
In transformers, which is a more recent advancement in deep learning architecture, a relatively modern innovation, self-attention mechanisms are used to deliver unmatched context comprehension.
RNNs are particularly suited for sequential data that is a norm in text-based documents. They may accordingly capture context from the sequence of words and are useful in tasks involving understanding the flow of text as useful in sentiment analysis or content classification.
CNNs are adept at dealing with spatial data and can be used to extract features from images, including documents. It can detect typical patterns in the way a document is laid out, such as headers, footers, or more general paragraph structures, so it is also useful for partitioning a document into logical sections or when the visual formatting contains helpful discriminative information.
The most recent revolution that designs the architecture of neural networks, Transformers have outperformed both RNNs and CNNs for performing a variety of natural language processing tasks. Unlike the RNNs and CNNs, which either process data serially or through localized filters, Transformers use self-attention mechanisms for the weighing of parts of input irrespective of their position. This enables the more sophisticated understanding of context and relationships within the document which is critical for complex textual analytic tasks.