Document data extraction is the task of extracting meaningful information from unstructured and/or semi-structured documents for subsequent use or storage.
Automated data extraction is the method of extracting information from unstructured or semi-structured data without manual intervention. This involves the application of AI/ML-based techniques for efficient and automated data extraction. The complete process of using intelligent tools to extract data from documents and process it to derive meaning and relevance is termed Intelligent Document Processing (IDP).
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The role of documents in business processes
All businesses, irrespective of competency, turnover, size and work style, deal with a plethora of documents. Business documents differ from general documents in the following ways:
- Business documents contain predefined data. For instance, invoices contain the name of the issuing company, amount, and taxes. Employment letters contain the name of the employee, date of employment and salary details.
- Business documents are usually created using predefined layouts. The layouts would differ from company to company, but largely remain invariant within a well organized company. For example, certain areas of any document from a specific company are reserved for logos, in general, and other sections are reserved for data relevant to the type of the document .
- Each type of business document is associated with one or more specific keywords. For example, the word “carrier” is almost always present in a waybill adjacent to the name of the transport company.
The ability to categorize data from documents, and access and analyze data methodically is key to intelligent decision making in a successful organization.
As the business expands and transforms over time, a heterogenous world of information and data becomes available to the company, which, if properly mined, can benefit the company in staying competitive. This where the automation of the document processing and data extraction processes add value to the enterprise.
Intelligent Document Processing (IDP)
Document processing is essentially a sequence of steps that includes,
1. Transformation of a document from a material one to a digital version
2. Discerning the structure of the document and identifying key content
3. Establishing the category of the document through identification of defining features
4. Extracting the content from the document
5. Using the data towards productivity
Intelligent Document Processing (IDP) is the use of Artificial Intelligence (AI) Tools in document processing. It combines data extraction with file management and orchestration through the interplay of AI technologies like computer vision, machine learning and natural language processing to not only extract data, but also categorize, validate, and store them. These tools may work independently or in synergy to extract unstructured data from various kinds of documents and convert them into structured, meaningful information.
The process of IDP starts with the collection of data from various sources and documents. This data can be gathered from multiple channels and in different formats. OCRs, ICRs and computer vision are used for capture of data from documents. During this process, a digitized version of the document, or “digital twin,” is created for subsequent processing.
The next step is the classification of elements such as names, amounts, IDs, etc. Data analysis involves the processing of human language and therefore automation of document data extraction involves the use of Natural Language processing (NLP) techniques such as Named Entity Recognition (NER), coreference resolution, relation extraction (RE), template filling, and semi-structured information extraction (e.g., table extraction). IDPs are often provided with a library of pre-trained extraction models or pattern matching tools that help in characterization.
Finally, the IDP validates and verifies the data — either through learning, or with humans in the loop— before integrating it into a target system. This final step may include processes such as Robotic Process Automation (RPA) that transfer files between databases, performing follow-up actions like emailing receipts, etc.
The output of a good IDP includes not only the relevantly isolated and classified data from documents but also useful, actionable metadata about the analyzed documents. A good IDP would provide a vision of the larger context and relevance of all documents that pass through a company’s workspace.
How to automate data extraction for IDP
While Optical Character Recognition (OCR) tools have been used extensively in the recent past to extract digital data from documents, IDP differs from legacy OCR in that while the latter simply converts scanned image into text, the former extracts, categorizes and exports relevant data for further processing using AI technologies. When the manual activities prior to and following simple text extraction by OCR are also performed by smart machines, it becomes an IDP system.
Data extraction from documents involves the acquisition of raw data from documents for further processing. Once the documents are imported into the digital platform of choice, data extraction software scans and captures the required data.
Legacy OCR methods are non-discerning data extraction methods, i.e., they extract all data from the document and include all information present in the source document. Non-discerning data extraction requires further human intervention to understand the document and process the data as required..
Example of non-discerning data extraction from a document using legacy OCR
Next generation OCR tools extract data from pre-specified zones in the document. This is a little more discerning than the original simple OCR.
The operation of zonal OCR
In recent years, OCR tools have approached entire IDP systems in their functionalities, in that they are equipped with AI tools for intelligent capture of data from documents. The Nanonets OCR API uses state-of-art AI algorithms that allow the design of custom OCR models. Data can be uploaded, annotated, and the model can be trained easily and seamlessly integrated with existing systems. For training and learning of the AI models, a certain amount of human validation would be required to test a small sample of the model’s performance to check for accuracy or incorporate course correction to the algorithms for more accurate data understanding. In such software, the line between OCR and IDP is blurred.
Challenges to automated data extraction
The main challenge to automated data extraction is the variety of document types from which the data must be extracted. Not only does the context of the document differ, but also the structure; documents could be highly-structured, semi-structured, or unstructured. While zonal OCRs can be programmed to extract data from semi-structured and structured documents, they fail with unstructured documents. Unfortunately, almost 95% of businesses handle unstructured data. Even in semi structured documents, the layout structure could vary for the same type of documents as well with varied locations of logical objects, such as names or dates.
In many cases, data must be extracted from Visually Rich Documents (VRDs), in which the layout and visual representation of information is critically associated with understanding the whole document. AI-enabled data extraction tools can handle VRDs and unstructured documents. Such tools use statistical methods, neural networks, decision trees, and rule learning techniques to intelligently capture relevant data irrespective of their position in the source document. AI-based data extraction tools can be trained to collate data in a sensible manner that make them suitable for post processing operations.
Data security is another area that can be challenging in automating data extraction. Financial data, for example, are highly sensitive and data security must be ensured by organizations that use automated data entry tools for data management. Many data entry tools like Nanonets, come with a robust technical assistance team that can help overcome the challenges and harness the full potential of automated data entry operations.
Incentives to automate document data extraction
A survey by the MIT Initiative on the Digital Economy (MIT-IDE) showed that business management practices based on data collection correlated to better performance in a wide range of operational settings. Decision making based on data mined from various sources was found to be associated with a statistically significant productivity increase of 3%. Naturally, the market for IDP solutions is expected to reach $4.1 billion by 2027.
According to a recent report by Allied Market Research, titled, “Data Extraction Market by Component, Data Type, Deployment Model, Enterprise Size, and Industry Vertical: Opportunity Analysis and Industry Forecast, 2020–2027,” the global data extraction market that was valued at $2.14 billion in 2019, is projected to reach $4.90 billion by 2027. MarketWatch reports that data extraction software has reshaped different industries such as BFSI, manufacturing, retail, and others by enabling digitalization across these industries.
Customers who have used the Nanonets AI-supported data extraction software have reported benefits of 80% savings in accounting costs and 3-5 times ROI in a payback period of 3 months. Expatrio uses Nanonets to save 95% of time spent on manual data entry and Advantage Marketing scales its business 5x times using Nanonets automation.
Available technologies for automated data extraction
Apart from data extraction leaders like Nanonets, there are open source tools available for automated data extraction:
- Tesseract: Tesseract initially developed by HP has been taken over by Google. The following blog on Nanonets provides a comprehensive review of the same https://nanonets.com/blog/ocr-with-tesseract/#introduction
- OCRopus: OCRopus is a collection of tools used for performing OCR on images. It is a full GUI engine and can optionally use tesseract in the backend for performing OCR.
- Calamari OCR: Calamari OCR uses deep neural networks implemented in TensorFlow.
- Connectionist Temporal Classification (CTC) is also a neural-network based data extraction tool that is useful for tasks like online handwriting recognition or speech recognition.
Commercial software like ABBYY and DocParser are other AI-driven data extraction tools that have their own competencies.
The choice of a data extraction software for a company depends on:
- Hardware requirements – Does the company have the appropriate technology infrastructure to run the software efficiently?
- Need for auxiliary hardware such as signature pads, scanners, etc.
- Cost of the software and its maintenance.
- Availability and cost of technical support.
- Data storage and backup options.
- Need for training of personnel to use the system.
- Level of integration with existing software products.
Take away
Automated extraction of data can benefit companies by lightening the workload, increasing productivity, and affording competitive advantage both in terms of bottom lines and employee satisfaction.
Automate Document OCR with Nanonets
Here are some more than 350 automated document OCR use cases that Nanonets can support.
