Key Technologies That Drive Intelligent Document Processing
Businesses of all sizes are seeking methods to streamline operations and make processes more efficient as the world transitions to a digital-first society. Businesses are under pressure to increase productivity, deliver top-notch customer service, save costs and adhere to regulations in order to keep up with market demands. This is also crucial in the area of document processing, where businesses are attempting to automate the tiresome and time-consuming process of manually organizing and processing papers. Intelligent document processing, a technology that combines machine learning and artificial intelligence, was developed to assist businesses in automating their document processing procedures. The information included in documents can be used by IDP solutions to extract data from them, classify them and even start activities.
Rise of Intelligent Document Processing
The requirement for businesses to automate document processing activities and derive insights from unstructured data gave rise to intelligent document processing. Initially, organizations relied on labor-intensive, error-prone manual data entry and processing. Businesses could automate the text recognition and extraction process with the introduction of Optical Character Recognition (OCR) technology. Eventually, as computer vision and natural language processing advanced, IDP solutions got more complex, enabling machines to comprehend the context and organization of documents. Today's IDP systems automate document management procedures and draw insights from unstructured data by combining OCR, NLP and CV technologies.
Breakdown of Key Technologies Driving Intelligent Document Processing
Intelligent document processing provides efficiency to organizations through:
Optical Character Recognition
This technology enables computers to read text from scanned or imaged documents. OCR software scans the paper, recognizes the characters and transforms them into text machines can process. This technology enables intelligent document processing solutions to extract data from unstructured documents such as invoices, contracts and forms.
Natural Langauge Processing
This is a part of AI that focuses on using natural language in communication between people and machines. Intelligent document processing solutions can comprehend the context of a document using NLP, including the meaning of individual words and phrases. IDP can classify documents according to their content, extract details like names, dates and locations and even detect sentiment using NLP.
Recommended by LinkedIn
Computer Vision
This technology enables machines to decipher and comprehend visual information obtained from the external environment. In the context of intelligent document processing, computer vision enables robots to examine and comprehend a document's structure, format and layout. Tables, graphs and other important data points can all be located utilizing computer vision by IDP solutions.
Benefits of Intelligent Document Processing
IDP can automate document processing, which has many advantages for businesses, such as:
● Lowering the demand for human workers to input and evaluate data and increasing operational efficiency
● Reducing the chances of improper data collection from various sources
● Collecting data thoroughly and from various sources, including previously untapped sources that provide useful data
Intelligent document processing in business is revolutionizing how documents are handled and processed. IDP systems can extract data from unstructured documents, classify them and take necessary actions depending on the information inside the documents by automating document processing workflows and employing AI and ML technologies. The growth and evolution of this industry will persist as advancements in cutting-edge technology continue to automate and streamline document management procedures.
AI has improved the accuracy of Intelligent Document Processing, but the total cost of ownership still raises concerns. Consider processing 100,000 invoices, each with 18 key-value pairs. i.e., 1.8 million fields in total. With traditional OCR at 80% accuracy, analysts verify all fields (2 seconds per field = 1,000 hrs) and correct 20% errors (360,000 fields at 5 seconds each = 500 hrs). Total effort: 1,500 hrs, costing $24,000 at $16 per hr (usual rate offered by companies). AI systems improve accuracy to 92%. But analysts still need to verify all 1.8 million fields (1,000 hours), but they now correct only 8% errors (144,000 fields at 5 seconds fixing time for each = 200 hours). Total effort drops to 1,200 hrs, costing $19,200. Saving $4,800. However, using a modern AI system itself costs an additional $10,000 (typically 10 cents per invoice for 100,000 invoices). This brings the total to $29,200, which is $5,200 more than the OCR-based approach. Despite higher accuracy, the high cost of AI systems, combined with the need for full manual verification, makes the overall approach less cost-effective. Full TCO analysis here: https://scryai.com/research-articles/current-ai-systems-cannot-replace-humans-easily-their-tco-is-enormous/
Interesting to see how your title image appears in a similar environment within a comparable context. Check it out: https://a.storyblok.com/f/177574/x/7aa797f189/case-study-parsee-corporate-knowledge-access-ai-chatbot.pdf