Revitalizing OCR Using Innovative AI and Deep Learning Algorithms
In this article, we discuss how OCR technology is utilized in numerous use cases and how it can further be enhanced by using AI and deep learning algorithms.
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In this digital-oriented age, technology is advancing in such a way that it is paving the way for information extraction from handwritten documents or scanned images called Optical character recognition (OCR) data extraction. Thankfully, OCR technology has a wide range of applications to automate and enhance business operations. OCR technology allows data extraction from bank statements, product sheets, passports, contracts, receipts, invoices, utility bills, and a variety of other documents.
In 2020, the global OCR market size hit the figure of USD 7.46 billion and it is expected that from 2021 to 2028, the market size will expand at a Compound Annual Growth Rate(CAGR) of 16.7%. No doubt OCR technology performs accurate and reliable data extraction and plays a crucial role in financial infrastructures, insurance claim processing, legal and logistic documentation, but OCR systems cannot perform well with unstructured documents. IDP utilizes numerous AI technologies to pre-process, extract and post-process information to deal with these OCR shortcomings.
Obstacles With OCR Processes
Indeed OCR technology enhances text recognition, face recognition, and numerous business operations, but there exist certain limitations that are faced by traditional OCR engines. As a result, those engines are unable to perform data extraction accurately and efficiently.
One major obstacle is the utilization of templates. The utilization of templates in OCR engines as a processing framework for the identification of various document entities proves to be a hurdle. This is because these frameworks do not deal with variations in the document structure that keep on occurring with the passage of time. Templates are not efficient enough to deal with complexities such as tabular data, a mixture of handwritten and printed data, and format variations.
Along with the challenge of document variation, images can bang OCR performance into a brick wall. The receipts have numerous handwritten stamps that adversely affect the OCR data extraction process. Financial services are only capable of achieving 60% of accuracy during the version process of long payment receipts into OCR.
Contrary to this, IDP assumes that documents are not fixed which revolutionizes everything about how data extraction is performed. This extraction process is AI-powered with no templates involved.
AI for Receipt Data Extraction
As mentioned above, financial companies can achieve only 60% of accuracy during receipt extraction. Does the question arise about how to handle receipt conversion?
The correct machine learning model is utilized to increase the accuracy of the receipt extraction process from 60% to 95%. The extraction application is developed in such a way that it helps to categorize documents before performing the data extraction process.
The processing steps are as follow:
- Send numerous pictures of receipts to the application API as URLs.
- With the help of AI algorithms, stitch the pictures together.
- Perform data extraction process on key fields and line items from stitched pictures with the help of extraction module.
- Perform data extraction process on field coordinates from images and handwritten text with the help of a machine learning (ML) module.
- After all these steps, the extracted OCR, stamp details, and handwritten text are combined into one JSON response so that client can use that.
Numerous OCR Engines
OCR is successfully utilized to perform data extraction processes on complex documents such as tables, electrical layout charts, and engineering diagrams. But other than that, we can use OCR in various different ways. Optical Character Recognition OCR is used for converting an image into some readable text but there is no single OCR that is good for all jobs.
Each Optical Character Recognition (OCR) engine has its own robustness. One might particularly work for images from mobile devices while the other might work well with scanning documents. This means that utilizing a single OCR engine for a wide range of use cases reduces the accuracy rate and increases manual labor costs.
Numerous experiments and hypotheses conclude that the incorporation of machine learning algorithms in OCR engines to deliver better results. Now it’s up to you to make the best choice for job performance enhancement.
Deep Learning To Deliver Better Results
Let us consider an insurance company that converts contacts. Businesses can benefit if the firm is capable enough to analyze contact insights and risk exposures. That’s when deep learning is required to be implemented. Deep learning is a terminology that is utilized for a multi-layered neural network that imitates the functionality of human brains. An insurance company can surely utilize deep learning algorithms to gain risk exposure and insights from a large number of contracts accurately and efficiently.
It is concluded from continuous feedback that deep learning algorithms enhance data extraction procedures and deliver better results to businesses.
Following are some points to ponder to add more value to businesses:
- Pay attention to how artificial intelligence can extract insights and information from unstructured documents.
- Reframing organization principles that enhance your solution.
- Refrain from solving for structured documents that fit into a template.
There’s a major difference between having an insight on something like a traditional OCR problem and considering it as an AI-powered IDP problem that can considerably prove to be beneficial for business enhancement. OCR can be embraced as a disruptive new technology for the automation of traditional business processes. The emergence of artificial intelligence has led modern enterprises to increase their expectations about what automation is capable of achieving.
The emergence of optical character recognition technology with artificial intelligence proves to be a winning strategy for both management and data capture. AI-powered OCR tools prove to be transformative technology and can leave a positive impact on companies who consider embracing them. In the wider topic of digital transformations and technological advancements, AI-powered OCR tools are sleeping giants. Hence technologies that reduce cost and have a high rate of accuracy and efficiency are always preferred by business and financial infrastructures.
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