Many organizations face a challenge today while unsheathing their compound unstructured data. These challenges seize the opportunities for focusing on further business advancement and improvements.
Organizations try to deploy a manual workforce for their extraction of unstructured data. However, it turns out to be a humdrum routine of work. Deploying human capital for these tasks is not the best solution for any organization. Organizations demand skilled human labor for manual extraction, and at the same time, face serious obstructions and reliability problems. Overcoming these challenges involve automated solutions through the use of Intelligent Document Processing (IDP).
IDP achieves the greatest levels of coherence and precision for data management. IDP involves self-operating document controlling tasks which turns out to be the most reliable, scalable and agile.
Exploring Critical Capabilities for Intelligent Document Processing
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AI Native Platform
Extraction accuracy has been a challenge for organizations when deploying Optical Character Recognition (OCR) as OCR alone can’t achieve precision while extracting data. OCR legacy platform turns out to be brittle, Especially, when dealing with complex, and unstructured documents.
The solution for this would be selecting an AI-native platform for saddling the true AI power, instead of struggling with fragile systems. AI-native platforms are more practical, supportive and operational as they are designed to use for the complete accomplishment of tasks.
Moreover, these platforms involve the employment of several AI technologies like deep learning, Natural language processing, machine learning, computer vision, etc.
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Learns and Improves
Business processes mutate and shift over the passage of time. Processes go through comprehensive changes, changing the overall structure of an organization’s data. For complete automation, there is a requirement of intelligent structure capable of adapting towards these changes in documents, and use cases.
Many a time, organizations have to deploy manual templates for adaptation of changes, which turns out to be a time-consuming task. To overcome the problem of constantly changing data within an organization, Machine Learning and Deep Learning needs to be deployed.
IDP processes are capable of adapting towards changes on their own with minimal human efforts. The AI technology used by IDP learns continuously and improves its functionality.
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Template-Free
Optical Character Recognition is all about templates. Templates are referred as a rule-based system for finding the data, and regulating it for extraction. There is a need for organizations to manually set templates while deploying OCR alone.
IDP solutions have the potential to extract data free from the manual template extraction method. The processes involving machine learning are flexible enough for working on dynamic data extraction methods, without the need for manually coded templates.
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Steering Unstructured Documents
IDP solutions are capable of handling and mantling all types of data formats like structured, unstructured, and semi-structured document types.
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Steering Complex Documents
Many a time, an organization involves documents having a complex and untraceable structure. Through the use of IDP’s AI capabilities, organizations are able to extract information even from complex structured documents.
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N-level Classification
Classification of documents turns out to be a mundane and time-consuming task when it is done manually. IDP solutions automate the classification of documents, as it is crucial to classify documents before extraction. IDP solutions involve Robust Classification.
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Enterprise Scalability
IDP solutions make scalability easier for organizations. It is able to handle data fluctuations, and understanding new as well as old documents. Thus, helping organizations to scale up their operations.
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OCR Engine Agnostic
Many a time, OCR engines for capturing images from text get locked towards a specific vendor. To overcome this problem, OCR Engine Agnostic is required.
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Easy to Manage
Some issues with the employment of pioneering automation systems include the employment of IT, data engineers, data scientists, etc.
However, IDP solution systems are capable of allowing organizations to easily manage the IDP processes. Inculcating agile and flexible methods for managing the IDP solutions.
