Inefficient paper-related tasks can immensely impact a company’s productivity. 46% of employees spend their time on such tasks which leads to a chaotic workflow. To mitigate this challenge, businesses have adopted optical data extraction solutions to streamline document-related processes.
These solutions leverage cutting-edge AI technology based on computer vision, NLP, and ML to fetch meaningful information from scanned documents and other textual information.
Major industry players have invested their resources in developing advanced OCR services, especially in the field of handwritten text recognition to automate it. The recent innovation in deep learning has transformed the way in which information can be processed. Not only this, but OCR technology can also be used to extract data comprehensively and accurately. By utilising these advancements, companies can increase their productivity and optimise their workflow.
How Does OCR Data Extraction Work?
It has been declared that 93% of enterprises face challenges in encountering papers and information. Historically, handling key business activities involved managing a considerable amount of handwritten documentation. It has now been successfully substituted by digital documents through optical character recognition data extraction.
The procedure of data extraction utilises automated solutions to transform unstructured data into a format that can be easily processed by humans. There are several types of data that can be extracted when working with scanned documents.
Types of Data for OCR Technology
OCR technology is a famous tool for extracting data from scanned documents. Moreover, it can also extract different types of data that have unique requirements and challenges. Below are a few of the most commonly extracted types of data using OCR technology.
1. Text Data Extraction
The most challenging and crucial task in OCR data extraction is extracting data from scanned documents. These documents are ordinarily displayed in a visual format, making it difficult to extract text. The complexity of handwriting, the quality of the scanned documents and the type of text further add to the difficulty.
However, OCR technology can transform image data into text data which can be utilised for different applications.
2. Figure and Table Data Extraction
Tables and figures are the most often used methods for data storage and OCR can easily extract data from them. But, it’s not the only technology that is required to extract tables.
Furthermore, there is visual data in tables such as lines and other elements. Computer vision helps structure this data for further processing, achieving high precision in table data extraction. Retrieving data from figures on a scanned page is also crucial as bar and pie charts usually contain crucial information for the scanned documents.
3. Key-Value Pair (KVP) Data Extraction
KVPs are alternate formats used to keep data in documents. Also, they represent two different values at the same time such as colour (the key) and purple(the value). Contrary to tabular representations, KVPs usually exist in illegible formats and can be partially handwritten. That’s why identifying underlying structures to automatically accomplish KVP data extraction is continuous research.
Data Extraction Technologies
Data extraction is the advanced procedure of obtaining essential information from reliable sources such as files, databases, websites and documents. Companies can perform this task manually or automatically, involving locating specific pieces of data from a digital document.
While retrieving information from a scanned invoice or ID card format, OCR service provides a crucial set of tools. It helps in recognising handwritten or printed text. Before extracting data, the digital data needs to be retrieved. OCR helps with this task by processing the pixels in the ID card and converting them into a digital format.
Data extraction then encounters the labels like name, date of birth and captures the required information. It’s worth noticing that OCR isn’t always necessary for data extraction.
In a few cases, essential documents such as PDF files, can go through the data extraction process without OCR. As they were created from a digital file, these documents already include a digital text layer. It helps in making the textual data accessible without requiring OCR technology.
Intelligent data extraction usually relies on two key processors in deep learning: Natural Language Processing (NLP) and Optical Character Recognition(OCR).
Optical Character Recognition(OCR)
OCR is a procedure that transforms images of text into machine-encoded text and machine-readable formats. OCR technology usually requires additional methods such as computer vision to detect tables and key-value pairs(KVPs) using line and box detection techniques.
Natural Language Processing(NLP)
NLP plays an essential role in analysing the meaning of the extracted data. The formation of deep learning has contributed immensely to the data-extraction pipelines. Not only this, but it also brought advancements to computer vision(CV) and NLP fields.
At last, the implementation of OCR data extraction solutions has greatly transformed the way companies manage their document-related procedure. The use of AI technology based on computer vision, ML, and NLP can fetch different types of data from scanned documents. It includes figures, text, tables and key-value pairs.