Automated Data Extraction’s Impact on Business Transformation

While there has been significant digitization of information communication within an organization, a large portion of the information is still transferred in its physical form - via paper – with consumers and other corporate parties. A sizable amount of legacy data is still stored on paper; digitizing this data would be of great benefit.

Traditionally, a huge pool of the human workforce, as part of standard operating procedures, infer, read and obtain data from these papers and input it into operational databases for further analysis. We have reached an age of automated data or information extraction thanks to the development of RPA, machine learning, and Artificial intelligence technology. These innovations have a huge potential to alter how businesses operate and do business.

Automation for Transforming Business

With the use of technology, it is now feasible to extract important information from documents by comprehending their layout, content, and associated labels and values. These technological elements, which are either open source or available for a fee, comprise:

Computer vision - The document has been digitized into images, each representing a page. As a result, computer vision program packages are initially used to comprehend and identify each part of the image that is of interest, like tables, boxes, paragraphs, handwritten text, logos, etc., utilizing methods like contouring and thresholding. The open source program OpenCV is among the most popular.

OCR (Optical Character Recognition) - When a particular area of interest is found, OCR libraries collect all text characters that are available there. A variety of character sets in various sizes and fonts were used to train the Ml algorithm for the OCR libraries.

Natural Language Processing – Any contracts that contain data in the form of clauses are interpreted using natural language processing (NLP). The objects and their properties can be more easily identified. Several well-known open source packages with pre-trained grammatical interpretation and the capacity to extract values from trained entities include NLTK, Spacy, and RASA.

Intelligent Character Recognition – ICR technologies are applied to recognize handwritten characters. It uses artificial intelligence algorithms developed using many annotated actual text values and handwritten text images as training data. Although corporate solutions provide ICR, one can utilize preexisting open source models that have already been trained and add specific training on top of it to address a particular issue.

A combination of the aforementioned fundamental technological elements is used for extracting data.

Challenges in Data Extraction

Documents are typically practically accessible in image form. The process of extracting information from and transforming data into a digital representation is typically complicated by various noise and quality factors. Watermarks, pen scribbles, wrinkles, tears, discoloration, smudging, scribbling on printed text, stamps imprinted on the text, dark backgrounds, irregular white-and-black grains, printed with low-contrast or colored ink, faded ink, and low scan dpi are some examples of these. Tables can be complicated with merged and split cells, tilting tables, ambiguous boundaries, and many more variants. They may or may not contain grid lines.

Additionally, it is challenging to extract data from handwritten text due to cursive writing and the lack of distinct character segregation. Another difficulty in analyzing legal documents is drawing inferences from several connected sentences in a clause or section. Extraction of information from documents that don't have a defined format, like an invoice, presents its own issues in comprehending important information's layout and locations.

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