Managing documents in various formats, including PDFs and scanned copies, has long been a pain for businesses, particularly when dealing with large quantities of documents. However, recent advancements in AI have introduced a specialized branch known as document AI, which aims to alleviate these challenges. Document AI leverages the power of artificial intelligence to extract relevant data from a wide range of documents, including invoices and receipts.
The traditional manual handling of documents, such as manually reviewing and extracting information, has been time-consuming and prone to errors. Document AI offers a transformative solution by extracting data from documents with high accuracy.
The document AI models can intelligently analyze documents, identify key data points, and extract them accurately without human intervention.There are some companies like Intelgic who built a document AI platform for automating document processing workflow. The platform comes with pre-trained AI models for receipts and invoices.
The benefits of document AI extend beyond improved efficiency and accuracy. With the ability to process documents in large quantities, businesses can save significant time and resources that were previously devoted to manual document handling. This allows employees to focus on more valuable tasks that require human expertise, leading to increased productivity and streamlined operations.
Document AI technology is designed to be adaptable and scalable, accommodating various document formats and layouts. Whether it’s invoices, receipts, contracts, or other types of documents, the AI models can be trained and fine-tuned to recognize and extract specific information relevant to each document type. This flexibility makes document AI applicable across industries and sectors, benefiting businesses of all sizes.
There are some free and open source AI models available for developers and researchers for experimenting document AI technologies.
This is an OCR free AI transformer proposed by Geewook Kim and colleagues, they introduced the Donut model. Donut is composed of two main components: an image Transformer encoder and an autoregressive text Transformer decoder.
Its purpose is to tackle document understanding tasks, including document image classification, form understanding, and visual question answering. By combining these two Transformers, the Donut model demonstrates promising capabilities in comprehending documents without relying on traditional Optical Character Recognition (OCR) techniques.
In the paper titled “LayoutLM: Pre-training of Text and Layout for Document Image Understanding,” authors Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, and Ming Zhou introduced the LayoutLM model. This model offers a straightforward yet powerful approach to pretraining text and layout information for tasks related to document image understanding and information extraction. Notably, LayoutLM demonstrates exceptional performance in various downstream tasks, including form understanding and receipt understanding, achieving state-of-the-art results.
DiT: Document AI
In the research paper titled “DiT: Self-supervised Pre-training for Document Image Transformer,” Junlong Li, Yiheng Xu, Tengchao Lv, Lei Cui, Cha Zhang, and Furu Wei proposed the DiT model. Building upon the self-supervised objective of BEiT (BERT pre-training of Image Transformers), DiT leverages 42 million document images to achieve state-of-the-art results in various tasks.
These tasks include document image classification using the RVL-CDIP dataset, which consists of 400,000 images categorized into 16 classes. DiT also excels in document layout analysis using the PubLayNet dataset, which comprises over 360,000 document images generated through automatic parsing of PubMed XML files. Furthermore, DiT demonstrates superior performance in table detection using the ICDAR 2019 cTDaR dataset, which contains 600 training images and 240 testing images.
As document AI continues to advance, the technology holds promise for even more sophisticated document processing tasks. It can enable intelligent document search, automatic categorization, and data analysis, unlocking valuable insights from vast amounts of document data.
Document AI is a game-changer for businesses grappling with the challenges of manual document handling. By automating the extraction of data from documents, it offers increased efficiency, accuracy, and productivity. Embracing document AI empowers businesses to streamline their operations, enhance data management, and leverage the full potential of their document assets.