Comprehensive Guide to Document Processing: Technologies, Benefits, Challenges

Enterprises trusted and relied on manual document processing for a long time because of the fear of data compromises and limited budgets. However, pitfalls such as unscalability, high employee churn, lack of high-quality data, and inefficiencies directed people to invest in advanced document processing solutions. 

Optical Character Recognition (OCR-based) solutions helped businesses extract accurate data from simple documents. With unstructured data accounting for 80% of the digital data universe, OCR solutions couldn't innovate and offer high accuracy rates. This laid the foundation for Intelligent Document Processing (IDP), now a game-changer in the industry. 

This article discusses document processing, its core functions, the role of AI, use cases, benefits, challenges, and future trends in detail. We also explore a case study to understand how a document processing solution like Docsumo drives efficiency and accuracy. 

What is Document Processing? 

Document processing refers to analyzing physical documents, PDFs, and images, extracting relevant information, and converting them into machine-readable formats for storage, retrieval, and integration purposes. 

Traditionally, document processing referred to the manual method where data operators scanned pages manually to locate the vital data. However, advancements in technologies streamlined document processing and automated data extraction tasks.  

How Does Document Processing Work? 

Document processing ai involves different methods such as neural networks, computer vision algorithms, and manual labor. The analog to digital data conversion process involves the steps below that make document processing effective. 

1. Categorization and extraction of structure and layout

Document processing solutions are based on a specific set of rules. Once these predefined rules are defined, the team implements structure and layout extraction. 

2. Document information extraction

OCR or Optical Character Recognition, involves the scanning of documents from the manual documents and their transformation to data. HTR, or Handwritten Text Recognition is another intelligent character recognition technology used to recognize the standard text as well as different styles and fonts of your handwriting. This technology can be used in image-to-text converters to automatically extract text from image and documents.

3. Document error detection and correction 

The OCR technology is sensitive to errors, hence requiring a manual review post the data extraction. If a document cannot be processed or errors are recognized, it can be further flagged for a human review, and you can fix the errors with a manual entry. 

4. Document and data storage

The processed document is stored in an ideal format that lets it integrate with the existing business applications.

The Role of AI and Machine Learning in Document Processing 

1. How AI Transforms Data Capture and Analysis?

AI technologies help automate document processing tasks and relieve employees from tedious work. These include document ingestion, preprocessing, metadata extraction, validation, routing for approval, and data integration. 

Example: An identity verification company is looking to process identity documents. An AI-based document processing solution will automatically intake identity documents, extract identity data (name, address, age), and validate them with government databases. This automated document processing improves efficiency by 10X and lets employees concentrate on critical tasks.

2. Machine Learning Algorithms for Improved Accuracy

Document processing machine learning algorithms improve the accuracy of the extracted data by learning from previous outputs, feedback, and training datasets. They store the corrections in memory to prevent errors and improve accuracy while processing new documents. 

Example: A platform failed to extract a key-value pair in identity documents because of different word usage. With the help of feedback and consolidating input from diverse identity documents, ML algorithms would accurately extract it from the new documents.

Intelligent Document Processing Explained 

Intelligent Document Processing (IDP) is a sophisticated technology that automatically converts semi-structured or unstructured information into structured data. This automation eliminates manual data entry, reduces human errors, and improves productivity. 

IDP solutions use a combination of Optical Character Recognition (OCR), Machine Learning (ML), Natural Language Processing (NLP) algorithms, and computer vision technologies to automate repetitive tasks in document processing, extract accurate data, and improve capabilities over time. 

1. Technologies involved in IDP 

Let's discuss each of these technologies and its role in document processing: 

Optical Character Recognition (OCR) 

OCR technology is the base of an IDP solution that helps digitize paper-based documents. It converts images, PDFs, and scanned documents into editable and searchable texts that computers can read and process further. 

Machine Learning (ML)

Machine learning algorithms analyze historical data to identify patterns and trends and predict outcomes in current processes. For instance, ML algorithms are helpful in document fraud detection, predictive maintenance, customer segmentation, and e-commerce product recommendations. ML algorithms also help the model improve continuously, enhancing accuracy and efficiency. 

Natural Language Processing (NLP)

NLP algorithms analyze document text, understand the context, and extract relevant and accurate information. They help with sentiment analysis, language interpretation, named entity recognition (NER), topic modeling, text classification, summarization, and translation. 

2. Differences Between Traditional OCR and IDP

Although OCR technology is a part of an IDP platform, significant differences exist between traditional OCR and IDP systems. 

Data accuracy

Traditional OCR systems extract high-quality data with predefined rules. Any simple variation in documents can lead to errors and inconsistencies. Meanwhile, the IDP solution captures accurate data from any document using AI technologies. 

Automation extent

OCR technology requires manual intervention to create rules for extracting accurate data, detecting errors, and making corrections. Conversely, an IDP platform automates end-to-end document processing and detects mismatches in the extracted data through smart validations. 

Improvement

Traditional OCR solutions don't learn and adapt to new document formats. However, IDP platforms self-learn and adapt to different formats, improving their performance and ensuring high accuracy. 

Cost

Basic OCR solutions are mostly affordable, and IDP platforms are more expensive than the former. However, IDP solutions are more cost-effective in the long run despite the upfront costs.

Deriving insights

OCR platforms convert documents into editable texts. On the other hand, IDP solutions extract data, identify patterns and trends, and find unique insights that help devise effective business strategies. 

Integrations

Integrating OCR software with existing legacy systems and advanced software solutions can be complex. IDP solutions provide seamless integrations with existing systems and transfer data without errors. 

Limited language and font support

OCR systems support only a limited number of languages and font styles; deviations can lead to failure. However, IDP solutions support multiple languages and fonts and even recognize handwritten texts.

TABLE

What are IDP Core Functions?

IDP incorporates four core functions:

• Data Extraction 
• Classification and categorization of documents
• Data Validation
• Business Intelligence and Insights

1. Data extraction

IDP is a powerful tool that eradicates or minimizes human intervention for data extraction and manual data processing.

2. Classification and categorization of documents

Depending on their content and structure, it can automatically classify documents into various categories. Advanced document processing solutions can accept numerous documents and automatically classify them to be routed to the appropriate work queues. 

They accelerate document processing and eradicate the manual effort that might be cumbersome for smart automation.

3. Data validation

IDP evaluates the extracted data adhering to the business rules, document comparisons, and other sources. It is crucial to analyze the extracted data to ensure accuracy. 

The validated data is further sent to processing, and the data that fails in the validation is sent for correction.

4. Business intelligence and insights

Firms use IDP to identify the data they extract to gather insights, take action, and drive business decisions with insights. Ensure you enquire with the IDP vendor about their functionality since it varies.

IDP processes documents with image and text complexity. Text complexity includes mixed fonts, footnotes, text with images, multiple documents in one PDF, long documents, etc. 

Image complexity comprises graphs, tables, mixed meaning, complex structures, unusual elements, or noisy images.

Additionally, IDP processes unstructured documents that have changed format and location over time. For instance, these documents contain the same data point in various locations based on the document type, version, and source. 

Use Cases of Intelligent Document Processing 

Here are some of the most common situations where IDP could be the perfect fit you can utilize:

1. Payroll

The manual invoicing system and payroll processing system need digitization and automation. When you use Intelligent Document Processing, you can enable the configuration and utilize deep learning models to implement data extraction.

2. Insurance

With document processing, you can extract the data from various forms and assess the eligibility/coverage. It can also keep the documents consistent in line with the industry-relevant protocols and standards. 

Additionally, IDP can protect personal information and sensitive documentation.

3. Human resources

Automated Document processing can convert the employee days to the relevant insights to optimize hiring decisions and staffing management.

4. Fraud detection 

The tool can effectively aid the financial services, authorize the signature on financial checks, analyze the authenticity of transactions with high volume, etc. to check against discrepancies in banking.

5. Mortgage processing

The lenders process numerous paper documents every year. Intelligent Document Processing paved the way for instant and simple document retrieval and improved the speed of the mortgage filing process.

Benefits of choosing Intelligent Document Processing

If you use IDP, it gives you endless benefits compared to the traditional document processing system as follows.

1. Rapid data processing

With advanced automation, you can extract accurate information from unstructured data faster. It can minimize workflows by eliminating the manual operations involved. 

2. Unstructured documents processing 

IDP can be useful to implement the transformation of unstructured, structured, or semi-structured documents to enhance business workflows.

3. Exceptional data accuracy

Machine learning can enhance information extraction, document classification, and data validation to trigger the reliability and quality of processing. The accuracy of the workflows can be improved with low-code supervised training.

4. Improved security

IDP can store personal information and documents in a digital location. This security is very important in industries such as financial and healthcare segments with stringent regulations and compliance standards.

5. Minimized cost

Manual document processing is time-consuming and makes the process hectic. Automation can cut short the time minimize operational expenses and enhance staff utilization.

How to Implement a Document Processing Software? 

Implementing a document processing solution demands careful evaluation of vendors and adequate training for employees. Here's a detailed step-by-step guide to follow while incorporating a solution into the workflows: 

1. Assess document processing needs

Start by examining the current document processing workflows (manual or partially automated) and understanding the challenges, routine tasks, and percentage of automation required. Additionally, map out the number of documents that need to be processed per month, their complexity, industry regulations, and integration requirements. 

After assessing the needs, list the goals to achieve by employing a document processing solution: eliminating manual data entry, enhancing precision, reducing operational costs, or improving team efficiency. This will help you choose the document processing solution that aligns with the prerequisites. 

2. Choosing the right document processing tool 

Consider the different document processing tools on the market and evaluate each one individually. Some must-have parameters are adaptability and flexibility, high accuracy rate, scalability, high security and compliance, and integration capabilities with existing systems. 

Apart from these essentials, customization options to alter validation rules, data extraction fields, and workflows are critical for any organization. Finally, ensure the platform is easy to use and has an intuitive interface to help non-technical team members easily adopt the platform. 

3. Vendor reputation and customer support 

Filter the best document processing platforms from the extensive list, and now it's time to check these vendors’ credibility. Look through the vendors' websites for testimonials and use independent review platforms like G2 and Capterra to learn about their customers' experiences. 

If needed, ask for industry-specific case studies to understand how the platform successfully generated results for the client. Besides that, check if the document processing vendor provides user training and troubleshooting support. 

4. Check pricing models and calculate costs 

Check the pricing models of different document processing solutions: per-feature, per-page, per-document, or per-user-based charges, recurring payment plans, or volume-based pricing.

Secondly, calculate licensing fees, hardware requirements, and training, support, and maintenance costs. After calculating the total costs, finalize the tool that fits the budget without compromising the document processing requirements. 

5. Train employees and implement the solution into workflows 

Leverage user manuals and demo videos provided by the vendor to educate employees about the tool’s capabilities. Provide adequate training to employees about how to unleash the maximum potential of the document processing solution. 

Implement the solution into the workflows and run a test project. Upon successful implementation, start processing documents in batches and monitor the performance to find areas for improvement and further enhancement. 

Challenges of Manual Document Processing

Some challenges that enterprises might face while processing documents using software solutions: 

1. Handling complex document types

Documents with varying structures, layouts, and complex tables can be challenging for document processing solutions to process and extract data. This results in low-quality data, making it unreliable for business operations. 

2. Document quality 

Most document processing solutions rely on document quality to ensure data accuracy. In such cases, low-quality scans with blurred, skewed texts and noises can lead to inaccurate data extraction. 

3. Recognizing handwritten notes 

Basic document processing solutions may struggle to parse through documents and forms with handwritten notes. Also, handwriting style variations can lead to errors and discrepancies, another serious challenge for businesses. 

4. Security and privacy concerns 

Processing documents, extracting data, and storing them digitally increases the probability of unauthorized access and breaches. Therefore ensuring data security and privacy is a serious challenge, as compromises can lead to fines, penalties, legal issues, and financial losses. 

5. Scalability and integration 

Businesses processing a large volume of documents face issues when using document processing platforms with limited scalability features. Moreover, some platforms provide only basic integration capabilities, which cannot ensure smooth data transfer. 

Overcoming these bottlenecks demands a robust document processing platform with advanced capabilities. Only cloud-based, AI- and ML-enabled solutions can accurately process highly unstructured documents, provide high security, and facilitate seamless integrations.  

Case study: Successful Document Processing in Action

VeriFast, a Canadian-based income verification company, faced challenges processing hundreds of bank statements manually. Their legacy OCR solution also provided only 40% accuracy, delaying decision-making and impacting efficiency. 

With the help of Docsumo’s Document AI, VeriFast automated data extraction and processed bank statements 2X faster. 

The result?

• 6000+ employee hours saved monthly 
• Reduced document processing time from 60 minutes to 1.5 minutes 
• 90% accuracy rate 

Check out the complete Verifast case study for more information.

Future Trends in Document Processing 

Here are some future trends that will change the way businesses process documents in the coming years: 

a. Predictions for AI advancements 

AI technologies will advance further, facilitating real-time document processing, improving data analysis, and handling complex documents with high accuracy and speed. These advancements will make document processing solutions much like an asset for organizations. 

b. The growing importance of cloud-based solutions 

The shift towards cloud-based document processing solutions will continue to grow because of its high scalability, flexibility, and accessibility. Therefore, enterprises can access documents anywhere and safeguard data with enhanced security features. 

c. Customization capabilities 

Customization capabilities of document processing solutions will evolve, enabling enterprises to tailor the system according to their industry and unique document requirements. This will motivate the adoption of these systems across different industries and sectors. 

How Docsumo Transforms Document Processing for Businesses? 

Docsumo helps industries with simplified data extraction, bank statement and invoice processing, automated data extraction, lease agreement data processing, landing and shipping labels/ receipts processing, and more.

The major advantage of utilizing Docsumo document processing solution is the use of pre-trained APIs for certain document types like acord forms, bank statements, invoices, licenses, IRS forms, and so on. Hence, you don't have to spend more time training the models from the beginning.

Docsumo APIs consider duplicate data entries, missing values/fields, to eliminate the redundant rates. Once the data extraction by the APIs is complete, users shall review and approve the changes using the platform. Finally, users can also upload the documents in one go and implement the processing for later use.

FAQs

1. Why is Intelligent Document Processing (IDP) important? 

Intelligent Document Processing is critical as it facilitates the easy extraction and analysis of unstructured data from documents. It simplifies compliance, ensures scalability, increases accuracy rate, and improves productivity and customer retention, making it indispensable for today's businesses. 

2. OCR vs IDP: Which is the best solution? 

Standalone OCR document processing solutions require rule creation and can extract accurate data only if the document is structured. However, IDP solutions can automatically adapt to the layout and data position and capture accurate data from various documents.