Most technologies that the common man is aware of regarding document processing are based on relational database models. In the simplest terms, these models were good enough to automatically process documents that had predefined formats and were well-aligned as well.
However, we are living in an age where social media, among other platforms, is churning out casual yet important information in huge chunks. Most of this data is not as per any “prescribed” format, and the length varies from platform to platform. Consumer-oriented businesses require important insights from such data.
Thus, there should be a mechanism that can support the extraction and automatic processing of data from these sources. It is here that we require an artificial intelligence based-optical character recognition solution like KlearStack.
To formulate the use of structured and unstructured data alike, KlearStack uses technologies like artificial intelligence, machine learning, natural language processing, and text analytics. These technologies not only help our OCR solution to automate document processing but enable it with lesser, or at times even no, requirement of human intervention. The outputs generated are intelligent and accurate.
Table of Contents
KlearStack’s AI-Based Approaches To Automate Document Processing
● Optical Character Recognition
In essence, our OCR tool is built on the same conventional concept that most of us are aware of. But in practice, it is not even remotely similar to any ordinary OCR solution.
Our optical character recognition methodology is still focused on converting scanned documents into machine-encoded text and creating digitally editable documents. But with AI, the output generated can be revolutionized completely.
Even for this simple task, we use research-backed methodologies that allow us to implement the techniques that give better results. For instance, we use advanced binarization techniques in OCR, along with algorithms for handwriting recognition.
The algorithms are accurate enough to distinguish handwritten components from printed ones and can recognize and interpret them without the need of human intervention.
Multiple feature extraction, statistical feature extraction, skew correction, segmentation, and slant removal are some of the additional facilities that we provide in our optical character recognition offering.
● Robotic Process Automation
Our detailed research in natural language processing and machine learning has enabled us to develop cognitive robotic process automation. Traditional robotic process automation was centered around developing software bots that could complete rule-based tasks themselves. However, without having any “cognitive” component, the relationship between the tasks to be completed and its real-world dynamics was not understood well by the system.
KlearStack was well aware of this problem with optical character recognition methodologies. Therefore, we came up with the cognitive RPA model wherein we combined artificial intelligence with robotic process automation.
This not only allowed us to automate repetitive data extraction tasks for information processing, but also allowed us to produce valuable output that could be further used to generate actionable insights.
This way, our solution empowers organizations to automate document processing of unstructured and semistructured documents as well and also seamlessly integrate the OCR solution with their business analytics tools to utilize the information for strategy building.
● Name Entity Recognition and Other NLP Techniques
We are deeply invested in researching the role of different technologies like natural language processing, computer vision, machine learning, etc., to automate document processing. Named entity recognition is a technique in natural language processing that we utilize for our AI-based optical character recognition tool. It is one of the key drivers of our intelligent document processing (IDP) offering.
It is through the natural language processing techniques like these that KlearStack’s AI-based OCR solution is able to utilize sentence-level syntactic rules along with grammar and expression understanding to replicate human-level document processing. Named entity recognition is one technique that eases the automation task by accurately locating the “named” components which may be names of persons, places, organizations, etc.
To implement such methodologies, KlearStack has certain approaches that other tools are lacking. Our solution performs an automatic text pre-processing which makes the text easily readable for machine learning models.
Noise removal and tokenization are the sub-processes that this phase entails. This is followed by advanced multiple feature extraction. Once these features are extracted, they are directly passed through a NER model, which automatically highlights and recognizes named entities without fail.
Conclusion
The process of automate document processing requires a high level of efficiency in recognizing features of documents and then converting them into digitized text. This is particularly more difficult when you have unstructured data sets to deal with. KlearStack provides a resilient system to automate document processing that uses the advanced methods of NLP, RPA, Computer Vision, etc., to carry out the process.
Extensive research has been conducted on the importance of template-free solutions for automate document processing. However, such solutions are still a rarity in the market, and KlearStack is a proud developer of one such solution. To know more about our intelligent document processing services, contact our team today.
