Text Extraction And The Role It Plays In Digital Transformation

While there is a considerable amount of digitization while sharing information within a business, when communicating with customers and stakeholders, such information is still exchanged in its physical form (paper). There is still a lot of legacy content available as a physical text and converting it to digital will be extremely valuable.

80% of data in organizations are unstructured in form. Such data need to be structured in order to be made use of. Conversion of unstructured data into structured data from a physical form to a digitalized form is a step closer to Digital transformation.

Before digital transformation, data was in the form of physical entities such as paper, records, books, registers, and so on. These physical data sources were managed and processed by manual methods that were slow, costly and needed a lot of human resources.

Thanks to Digital transformation, we have stepped into a space where Machine Learning, and Artificial Intelligence technologies leverage intelligent automation solutions. These technologies have enormous potential to change the way businesses operate and do business.

Related Read: Extracting Text from Machine Drawing and Nested Tables – A teX.ai Success Story

Important documents in an organisation may include names, numbers, signatures and details that need to be preserved safely for a long period of time. Such documents need to be scanned and stored in some location.

Most of the documents that are scanned in organisations are saved as Pdf files or image files. Extracting the textual data from such scanned documents or images needs to be done to process the data and store it in structured formats.

Why does the data need to be structured? Only from structured data can we obtain insights that can help make informed business decisions to enhance productivity, ROI and many other parameters in a business.

Below, are some types of documents that need to be digitalized for smooth operations and data management within organizations. The data from these documents can be extracted by text extraction software to bring out actionable insights.

• Contracts: Contracts are all sorts of legal agreements. Key metadata attributes, specific text clauses can be extracted to obtain inference-based results. Some of the extracted data can be classified under specific categories as per your business requirements to analyse patterns, key words, and many more.
• Proof of Identity (POI) documents: Confidential details of clients, employees and stakeholders from their POI documents can be extracted for future purposes of classification, summarization or redaction of the data to make informed business decisions.
• Images: Many images are present in documents. These images might contain specific data that can be extracted by text extraction processes.
• Transactional records: Transactional records include invoices, quotations, KYC documents, bank statements and so on. Data from these documents can be extracted to store the details in a structured format for better understanding and organising of the data. From the extracted data, selected data can be redacted, classified or summarised as per business requirements.
• Tabular documents: Documents that contain tabular-data may contain important data such as price listings, product details, client addresses, and many more. these tabular data need to be extracted from documents for better analysis.
• Pictorial documents: Documents that have diagrammatic illustrations and representations of data can also be scanned from which data can be extracted.

Automatic text extraction solutions can help businesses understand the layout of a text, as well as its content, associated labels, and their values, and extract vital data from them to convert them into sensible and useful insights.

1. Computer Vision: Scanned documents may consist of images. Using thresholding techniques, contouring, and other techniques, text extraction software recognize and identify any image variable of interest, such as paragraphs, tables, logos, handwritten text, boxes, and so on.
2. Natural Language Processing: NLP solutions that can help identify keywords, phrases, sentences and other such deep learning processes can be implemented to extract text data from documents. Some NLP processes include: Sentiment analysis, tokenization, sentence breaking, categorization, chunking, etc…
3. Optical Character Recognition (OCR): OCR solutions can extract all text characters in a given area before a region of interest is found. These libraries have an AI model that has been trained with a large number of character sets in different fonts and sizes.
4. Redaction: Text redaction is another reason why documents need to be extracted. The extracted data can be automatically redacted using Automatic text redaction software that can identify and hide confidential information such as PII, PCI & PHI.
5. Classification & Summarization: Another technique that can be used on extracted data is the classification & summarization. These are results of implementing NLP techniques on the extracted data. Some of the use cases of classification are product categorization, social media reviews analysis, sentiment analysis, etc…

In most instances, records and documents are available in image formats such as jpeg, PNG, and more.

The extraction of information and the transmission of data to a digital format is complicated due to a lot of noise and low-quality elements.

Watermarks, wrinkled, pen scribbles, torn, de-coloured, stamps printed on the text, black and white grains, smudged, dark backgrounds, low-contrast or coloured ink printed, faded ink, scribbling on printed text, Poor scan quality are only a few examples.

Tables can have or not have grid lines, and they can become complicated with merged and spilt cells, tilted tables, ambiguous limits, and a variety of other derivatives. Furthermore, handwriting with cursive writing and no clear character separation makes extracting information from handwritten text difficult.

Another challenge is while processing legal documents, where the meaning from various related sentences in a clause or section may be unclear. Understanding the layout of key information in documents that do not have a fixed format, such as an invoice, as well as extracting information from these documents, presents its own set of challenges.

You might be interested to read: Why teX.ai Can Help You Build A Truly Customer-centric Business?

teX.ai, a highly personalizable and scalable text analytics software can automatically extract, redact, classify and summarize your organization`s textual data.

The data that needs analysis can be of any format including Pdfs, Images, scanned docs, word, excel, html documents, IoT files, websites and more.

• Text analysis can be done across all organizations irrespective of their domain.
• 15+ languages can be extracted, redacted, classified and summarized.
• ai can be deployed on cloud, on-prem or a hybrid of both
• Image pre-processing can be done for blur and unclear images
• Automatic text extraction, redaction, summarization & classification

Looking to extract actionable insights from text data? teX.ai is your all-in-one solution for all your text analytics needs. Talk to us now.