Here’s how Intelligent Document Processing can help in Data Transformation

Intelligent Document Processing can play a crucial role in achieving crucial results in the Data Transformation process and help the organization to remain competitive in the long run.

Intelligent document processing (IDP) is used to automate the processing of documents with the help of a specific set of business solutions that are based on deep learning technologies. Prominent among these technologies are computer vision, artificial intelligence (AI), and robotic process automation. With the help of these technologies, IDP extracts the unstructured data from a range of documents including emails, PDFs, texts, etc. and then processes and converts this data into a structured form that can be further used by different departments for decision-making purposes. The advantage of IDP lies in its ability to extract, categorize, and export data to different units for further analysis to meet their specific requirements.

Intelligent Document Processing Process and Steps

The entire process of IDP can be categorised into five different steps:

a) Pre-processing: In order to enhance the quality of the data, the IDP chooses from a range of pre-processing techniques at its disposal. Some of the significant techniques used in pre-processing step include noise reduction, de-skewing, and binarization among others.

b) Image Processing: Using the feature of computer vision, IDP first understands the structure and identifies key features contained in the document in the form of pictures, graphs, and texts.  Various techniques used in this stage include intelligent character recognition (ICR) and optical character recognition (OCR). Further, IDP might also create the digital version of the document for facilitating the process of machine-reading at this stage. 

c) Data Classification and Extraction: Two primary technologies used for data classification and extraction include machine learning (ML) and natural language processing (NLP). The classification engine feature of the IDP is responsible for carrying out these processes and to accomplish this successfully, IDPs often employ extraction models that have been pre-trained for delivering desired accuracy and precision.

d) Data Validation and Integration: Validation is among the most important steps and to achieve the desired accuracy on validation, IDP relies upon lexicons and a variety of external databases. The use of these resources also ensures that the data is ready to use and delivered to the recipients in the right format. Further, the validation process involves a Human-in-the-loop framework which takes care of the problematic data. In the process of integration, data is assigned to the different workflows and Enterprise Systems of the organisation and can be accessed by users across the functional domains and departments.

Advantages of Intelligent Document Processing

1) Data Essentialism: With enhanced integration of IDP, organisations can evolve in their understanding of different document categories and finally realize “what matters the most to them”. More and more organizations are realizing today that almost 45-50% of documents produced by them are noisy and this enhanced understanding can act as valuable feedback for departments from where these documents are originating. Accordingly, important changes can be made to reduce the unproductive things that eat into the valuable time and resources of the company.

2) Anomaly Detection: IDP helps in the identification of the document category and makes it possible for setting the limits for upper control and lower control for value pairs and keys. This will facilitate the development of a new workflow that can flag the error values at the origin besides alarming the process administrators about the potential error.

3) Efficient Searching: One of the fundamental benefits of IDP is its capacity to enhance the traceability of the document. This, in turn, dramatically reduces the time spent by the organization in searching the document. Further, an organization can couple IDP with a semantic search to make the document traceability even more efficient than before. For example, you can use the command ‘Find me invoices from Customer X which has a value of more than Y’ and the system will return the results in no time; thereby taking the level of productivity to a new high. 

4) Resource Savings: In an organisation, a variety of reports have been made for the consumption of different stakeholders. The only changing variable in these reports is the data with most other portions of reports remaining static in nature. To save a considerable amount of resources, an organisation can intersperse IDP with NLP to automate report production with final overseeing being done by human authority. 

5) Enhanced Transparency: IDP’s integration with the document repository of the company can enhance the transparency and authenticity of the information. The integration will result in a Single Source of Truth and by offering the right information to the right people at the right time, IDP can help organisations to achieve a sustainable competitive edge over their rivals.

Authored by: Kaustubh Kashyap, Chief Growth Officer (CGO), OpenTurf