[vc_row][vc_column][vc_column_text]Businesses run more efficiently when they have a document classification system fixed within their day-to-day operations. Not only are they able to process 10,000+ documents but are also able to reduce human error, expedite workflows, enhance customer satisfaction.
Consider an instance where an insurance company receives numerous insurance claims from policyholders in various formats, such as PDFs, scanned documents, and digital forms.
By using a document classification software, the company automatically categorizes these incoming documents based on their content, such as auto accident claims, property damage claims, health insurance claims, and so on.
This classification helps streamline the claims processing workflow, ensuring that each type of claim is routed to the appropriate department for review and processing, improving efficiency and reducing manual labor.
What is Document Classification?
Document classification is the process of categorizing and organizing documents based on their content, characteristics, or other relevant attributes. These documents are grouped into predefined categories to facilitate efficient storage, retrieval, and management of information. This classification process can be performed manually by human operators or automated using various software tools and machine learning techniques.
This lets you automatically sort through your files and organize them into specific categories based on their content, making it easier to find what you need quickly. This process helps businesses and individuals manage their data more effectively, saving time and reducing the hassle of manually sifting through numerous documents.
Related Content:
- What is Document Processing & How to Automate It?
- What is Intelligent Document Processing (IDP)?
- What is Document Digitization and How to Digitize Documents?
Types of Document Classification
Document classification can also be broadly divided into manual and automated methods:
- Manual Classification: In this approach, human operators review each document and assign it to a specific category or class based on their understanding of the document content and context. This method is time-consuming and prone to human error but may be necessary for certain specialized or complex documents that require human judgement.
- Automated Classification: This method involves using software tools, algorithms, and machine learning techniques to automatically categorize documents based on predefined rules, patterns, or learned features. Automated classification significantly speeds up the process, reduces errors, and is particularly effective for handling large volumes of documents efficiently.
While manual document classification can provide a high level of detail and precision, it suffers from two significant limitations that render it impractical: time consumption and subjectivity.
Imagine handling the vast content of a corporate intranet, the extensive regulations of a governmental institution, the comprehensive archives of a newspaper, or even the vast expanse of valuable internet data for a company’s business needs. Managing such colossal data volumes within a reasonable timeframe becomes an insurmountable task for human operators.
This is where the role of automatic document classification software becomes invaluable. It empowers organizations to efficiently organize and access their content whenever necessary. Automated systems offer scalability, speed, and objectivity, ensuring that the classification process is not only streamlined but also more efficient and reliable.
Looking to Automate Document Classification for Your Business?
Try KlearStack, a perfect AI solution that can turn your time-consuming document classification operations into a matter of minutes, with 99% accuracy and 70% cost savings.
No Regex Rules. Easy Setup. Cancel Anytime.
Schedule a demo session today!
Additionally, document classification is also divided based on the techniques used, which are as follows:
- Rule-based classification: This method involves establishing predefined rules or criteria to categorize; documents based on specific keywords, patterns, or metadata attributes.
- Statistical classification: Utilizing statistical models and algorithms, this approach involves analyzing document features and patterns to assign documents to various classes or categories.
- Machine learning-based classification: This method utilizes machine learning algorithms to train models on labeled data, enabling the system to automatically classify documents based on learned patterns and characteristics.
- Content-based classification: This approach involves analyzing the actual content of the document, including text, images, or other media, to categorize documents based on their semantic or contextual similarities.
- Hybrid classification: This combines multiple classification techniques, such as a blend of rule-based and machine learning methods, to achieve more accurate and comprehensive document classification results.
How Does Automated Document Classification Work?
Automated document classification works on the following levels:
- Identifying the Format: The initial step involves recognizing the format of the documents being processed, such as text documents, images, PDFs, jpeg, png, pdf, tiff, or other file types.
Recognizing the document format is essential because different formats require different processing techniques. For instance, images may need optical character recognition (OCR) to extract text, while text documents may need natural language processing (NLP) techniques for analysis. Understanding the format ensures the application of appropriate tools and methods for accurate classification.
- Identifying Document Structure: Understanding whether the document has a structured format (such as forms or templates with defined fields) or an unstructured format (free-flowing text without a predefined layout) is crucial for determining the appropriate classification approach.
Distinguishing between structured and unstructured documents is important. For structured documents like forms, data may be extracted from predefined fields, whereas for unstructured documents, NLP techniques may be used to identify key concepts and patterns.
- Identifying Document Type: Determining the type of document, such as an invoice, report, or contract, aids in directing the classification process and ensuring accurate categorization.
This identification helps in directing the document to the appropriate group or folder within the classification system. For instance, if the document is an invoice, it would be classified under the “Invoices” category. Similarly, if it is a legal contract, it would be classified under the “Legal Contracts” category.
Key steps involved in automated document classification are:
- Data Collection and Preparation: The system receives various documents from customers, such as account statements, loan applications, and identification documents. The system preprocesses the documents by extracting text, removing unnecessary symbols, and converting the data into a standard format.
- Feature Extraction: Relevant features are extracted from the preprocessed documents, such as keywords, metadata, or structural elements, which will serve as inputs for the classification model. In the case of your business documents, features such as account numbers, transaction amounts, and customer names are extracted, providing key indicators for the classification model.
- Training the Model: Using a labeled dataset, a machine learning model is trained to recognize patterns associated with different document types, such as “Bank Statements,” “Loan Applications,” and “Identification Documents.”
- Testing and Evaluation: The trained model is tested on a separate set of data to evaluate its accuracy and performance in correctly categorizing documents.
- Deployment and Integration: Once validated, the automated document classification model is integrated into your business system, enabling the system to automatically sort and categorize incoming documents, making it easier for your employees to manage and process customer information efficiently.
Methods of Document Classification
Document classification can be accomplished using various methods, including:
- Rule-based Classification: This method involves creating predefined rules based on keywords, metadata, or specific criteria to classify documents into different categories.
- Machine Learning Classification: Machine learning techniques, such as supervised learning algorithms like Support Vector Machines (SVM), Naive Bayes, or deep learning models like neural networks, can be trained on labeled data to automatically classify documents based on learned patterns.
- Natural Language Processing (NLP): NLP techniques can be used to analyze the content of documents, including text and context, to classify documents based on semantic similarities, language patterns, and contextual information.
- Clustering: This method involves grouping similar documents together based on their features or content, without predefined categories, to identify patterns and structures within the data.
- Hybrid Approaches: Combining multiple methods, such as integrating rule-based systems with machine learning algorithms or combining NLP with clustering techniques, can lead to more accurate and robust document classification results.
Benefits of Document Classification
With Automated document classification, businesses can achieve significant improvements in efficiency, reduce the occurrence of errors, and realize substantial cost savings, ultimately enhancing their overall competitiveness and profitability.:
Efficiency Boost
Automated document classification tools help you and your team organize and find information much faster.
It uses advanced algorithms to quickly scan, analyze, and categorize documents based on their content. This automated process eliminates the need for manual sorting, allowing you and your team to access the right documents instantly, leading to faster decision-making and smoother workflows.
Error Prevention
By automating the classification process, you can significantly reduce the chance of mistakes that can happen when sorting documents manually.
This is because automated document classification systems are designed to accurately categorize documents based on predefined rules and learned patterns. By minimizing human intervention, these systems significantly reduce the risk of human error that can occur during manual classification.
This ensures that your data remains consistent and reliable, providing you with a solid foundation for making well-informed decisions.
Cost-Effective Solution
Implementing automated document classification means you can save on labor costs and free up your team’s time to focus on more important tasks.
This cost-effective approach allows you to make the most of your resources, ensuring that your business operates efficiently and effectively without unnecessary expenses.
Looking to Automate Document Classification for Your Business?
Try KlearStack, a perfect AI solution that can turn your time-consuming document classification operations into a matter of minutes, with 99% accuracy and 70% cost savings.
Whether it’s texts, images, pdfs, emails, or more, KlearStack is a no-code platform with drag-and-drop feature that can automate & classify your documents easily, fast & securely.
No Templates. No Regex Rules. Easy Setup. Cancel Anytime.
Schedule a demo session today!
FAQs on Document Classification
What are classification of documents?
Document classification involves categorizing documents based on their content, purpose, or characteristics, enabling efficient organization and retrieval of information within a system.
What are the three classifications of documents?
Documents can be classified as public, private, or confidential, depending on their accessibility and sensitivity. Public documents are accessible to all, while private and confidential documents have restricted access based on the level of sensitivity.
Why is document classification important?
Document classification is an important process because it helps businesses and individuals manage their data more effectively, saving time and reducing the hassle of manually sifting through numerous documents.[/vc_column_text][/vc_column][/vc_row]
