Automated document processing refers to the use of software and technology to extract, classify, and organize data from documents without the need for manual intervention. This technology uses various techniques such as Optical Character Recognition (OCR), Natural Language Processing (NLP), and Machine Learning (ML) to automate the document processing tasks.
Automated document processing is becoming increasingly important for businesses and organizations as they strive to increase efficiency, reduce errors, and improve data accuracy. This blog will provide an overview of automated document processing techniques, including intelligent document processing, and explain the benefits of using these technologies. We will also discuss automated document processing with KlearStack’s advanced solutions.
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What are the Automated Document Processing techniques?
There are several techniques used in automated document processing, including optical character recognition (OCR), natural language processing (NLP), and machine learning (ML). The following are some of the most common techniques used in automated document processing:
1. Optical Character Recognition (OCR)
OCR is a technique used to convert scanned documents or images into editable text. OCR software reads the characters in an image and converts them into machine-readable text.
2.Natural Language Processing (NLP):
NLP is a technique used to analyze and understand human language. NLP algorithms can extract data from unstructured text, such as emails or social media posts, and classify them into categories.
3. Machine Learning (ML):
ML is a technique used to train algorithms to learn from data. ML algorithms can be trained to recognize patterns in data and make predictions based on that data.
What is Intelligent Document Processing (IDP)?
Intelligent document processing (IDP) is a form of automated document processing that uses artificial intelligence (AI) and machine learning (ML) algorithms to extract data from documents. IDP goes beyond OCR by using NLP and ML techniques to understand the content of documents and extract relevant data. The core components of IDP include:
- Document Classification: IDP software can classify documents based on their content, such as invoices, purchase orders, or contracts.
- Data Extraction: IDP software can extract data from unstructured or semi-structured documents, such as invoices or receipts.
- Data Validation: IDP software can validate extracted data by comparing it to known data or by applying business rules.
- Data Transformation: IDP software can transform extracted data into a structured format, such as a spreadsheet or a database.
KlearStack’s AI-Based Approaches To Automate Document Processing
KlearStack is an AI-powered intelligent document processing solution that leverages the power of IDP to extract data from documents. KlearStack’s advanced solutions can handle complex documents, such as invoices, purchase orders, and contracts, and extract data with high accuracy. KlearStack’s solutions can also integrate with existing systems, such as ERPs and CRMs, to streamline workflows and increase efficiency.
● 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.
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. If you’re planning to implement Intelligent Document Processing solution to automate your document processing operations, contact our automation experts today.