How AI and Deep Learning have Revolutionized Document Processing Automation?

The biggest challenge of digitizing data and speeding up document processing is that the majority of business tasks still happen on paper or PDF files. From getting signatures on a report card to filing for a loan there is a huge process of documentation that is highly time-consuming and requires a lot of manual processes.

Companies spend a lot of money and resources in managing these documents and keeping track of them. It becomes even more critical, especially during audits in all the highly regularized sectors like healthcare, banking, legal, supply chain, etc. So automating this process becomes of utmost importance.

Before getting into details, let's discuss document processing automation:-‍

What is document processing automation?‍

Document processing automation is the design of systems and workflows that assist in the creating electronic documents. These include logic-based systems that use pre-existing text and data segments to assemble a new document. Certain industries increasingly use this process to assemble legal documents, contracts, and letters. Automation systems allow companies to minimize data entry, reduce the time spent proof-reading, and reduce the risks associated with human error.

What is AI-based document processing?

AI-based document processing, also known as Intelligent Document Processing(IDP) refers to the use of artificial intelligence (AI) technologies to automate and optimize the processing of documents. The process typically involves the conversion of unstructured data from documents into structured data, which can be analyzed and used in various applications. This technology can be used for many different types of documents, such as invoices, contracts, forms, and legal documents. A Laravel Development company wit AI alignment can provide tailored solutions to enhance the efficiency and accuracy of document processing through advanced AI techniques.

The AI-based document processing workflow involves several steps:-

i) The first step is to capture the document using various technologies such as scanning, OCR, and image processing.
ii) Then, the document is analyzed and processed using machine learning algorithms to extract relevant information such as names, dates, addresses, and other key data points.

iii) The extracted data is then validated, structured, and integrated into the relevant application or system.

How are AI and Deep learning winning the game of document data extraction?‍

The document process automation is a need in the digital era. Much work was happening in this field but recent developments using cutting edge technologies like deep learning and Artificial Intelligence have completely revolutionized this domain.

The earlier approaches were more focused on extracting features from images using techniques like edge detection, Gaussian filters, etc which had many limitations in real-world use cases. However, with the enhancement of deep learning models, you do not have to explicitly extract features from the image using any pre-processing techniques, rather you need to train your model using input and output images and your model automatically learns features from those images.

For example: The above algorithm represents the most advanced model that uses Optical Character Recognition (OCR) service to extract the text and layout information, which allows you to work with native digital documents, such as PDFs, and document images (e.g., scanned documents).

Components of AI-based document processing

AI-based document processing involves several components that work together to automate and optimize the processing of documents. Some of the key components are:

1. Data capture

It involves capturing the document data using various technologies such as scanning, OCR (optical character recognition), and image processing. The goal is to digitize the document and create a digital version that can be analyzed and processed.

2. Data extraction

It involves using machine learning algorithms to extract relevant information from the document such as names, dates, addresses, and other key data points. The extracted data is then validated, structured, and integrated into the relevant application or system.

3. Natural Language Processing (NLP)

It involves using NLP techniques to analyze and understand the meaning of the text in the document. This includes tasks such as sentiment analysis, entity recognition, and language translation, often enhanced by prompt engineering tools that fine-tune AI models for more precise results.

4. Machine Learning (ML)

It involves using ML algorithms to learn from the extracted data and improve the accuracy and efficiency of the document processing workflow. ML algorithms can be used for classification, clustering, and prediction tasks.

5. Data validation

It involves validating the extracted data to ensure its accuracy and completeness. This includes checking for errors, inconsistencies, and missing data.

6. Data integration

It involves integrating the extracted data into the relevant application or system. This includes mapping the data to the correct fields, formatting it, and uploading it to the system.

7. Security

It involves implementing security measures to protect the sensitive information contained in the documents. This includes encrypting the data, restricting access to authorized users, and monitoring for security breaches.

Document AI workflow

Document process automation workflow comprises of following steps:-‍

1. Data Ingestion

The data source is the primary channel of extracting information (data), whether the data is structured, non-structured, or is in any other format. Data Ingestion is the process of reading data through various channels including PDF, Excel, Mails, Word, Scan file etc.

2. Data pre-processing

‍This step requires image and data pre-processing steps like cropping, noise reduction, and filtering which eases the data extraction process.

3. Data capture

‍One of the most critical steps of the whole workflow is extracting relevant information. OCR is one of the most advanced technologies and is backed up by different machine learning algorithms. Different computer vision models and libraries like CNN and OpenCV are available which help in detecting and extracting text.

4. Data classification/Indexing

‍After extracting information and text from the source, classification, or indexing that information according to the template is a major challenge. For instance: while extracting text from invoices, it is vital to differentiate Date, Amount, Name, and other fields from the text you have extracted. Here, Deep learning models come to the rescue that label the data according to its category and automate the whole process.

5. Data extraction

‍Now, the information that you extract from the above process could be in different formats and also could be text or an image. Techniques like NLP and computer vision contribute to understanding the underlying data.

6. Data validation

‍The most important step is the verification of data and the quality check. This step can be automated using a template-based approach.

‍Document AI business use cases

Document AI finds multiple use-cases in modern-day businesses. Here are some of them:-

1. Lending

Lenders can use AI-based document processing to automate the loan origination process. By analyzing financial documents such as bank statements, tax returns, and credit reports, AI can help lenders quickly assess a borrowers' creditworthiness and make informed lending decisions. AI can also be used to monitor loan portfolios and identify potential default risks.

2. Insurance

‍Claims processing is at the heart of every insurance company. Since customers make claims at a time of misfortune for them, customer experience and speed are critical in claims processing. Numerous factors that create issues during claims processing such as

• Manual/inconsistent processing: Claims processing often involves manual analyses completed by outsourced personnel.
• Input data of varying formats: Customers send in data with various formats
• Changing regulation: No insurance company has the luxury of not accommodating changes in regulation on time. This requires constant staff training and process update.

3. Logistics‍

Trade finance involves multiple parties coordinating and ensuring the delivery of goods and payments. Banks and companies communicate through letters of credit and other documents that need to be processed.

The processes that have been talked about above can easily be automated.

4. Healthcare

Healthcare providers can use AI-based document processing to improve patient care and reduce administrative burdens. AI can help providers identify potential health risks by analyzing medical records and recommending personalized treatment plans. AI can also be used to automate administrative tasks such as appointment scheduling and claims processing.

5. Commercial real estate

Commercial real estate companies can use AI-based document processing to streamline property management and lease administration. AI can help companies track lease renewals, rent payments, and property maintenance tasks by analyzing lease agreements, property records, and other documents. AI can also be used to automate invoice processing and reduce the risk of errors.

Related - What's the future of ai-based document processing?

Benefits of using AI-based document processing

Let's discuss some of the benefits of document ai in 2023:-

1. Increased efficiency

AI-based document processing automates the processing of documents, reducing the time and effort required to complete manual tasks. This increased efficiency results in faster turnaround times, enabling businesses to process more documents in less time.

2. Improved accuracy

The technology reduces the risk of errors and inaccuracies that can occur during manual document processing. This is particularly important for industries requiring high accuracy levels, such as finance, legal, and healthcare.

3. Cost reduction

Automating document processing tasks reduces the cost of labor, as fewer employees are required to complete the same amount of work. This also reduces the risk of costly errors, such as incorrect data entry, which can result in financial losses.

4. Enhanced data analysis

Using automated document processing enables businesses to extract and analyze large amounts of data from documents quickly and accurately. This data can be used to identify trends, patterns, and insights that can inform business decisions and strategies.

5. Improved compliance

It also helps businesses comply with regulatory requirements by ensuring that documents are processed accurately and securely. This reduces the risk of non-compliance, which can result in legal penalties and reputational damage.

6. Greater customer satisfaction

Document AI improves customer satisfaction by reducing turnaround times and improving the accuracy of document processing. This leads to a better customer experience, which can increase customer loyalty and retention.

7. Scalability

Finally, AI-based document processing systems can be easily scaled to handle larger volumes of documents as businesses grow. This enables businesses to process more documents without additional staff, reducing costs and increasing efficiency.

Top 10 Python Libraries to enable document AI

Here are 10 Python libraries that can be used to build a document AI solution in-house:

1. SpaCy

SpaCy is a popular library used for natural language processing. It can be used to extract entities, detect named entities, and analyze syntax in text.

2. PyPDF2

PyPDF2 is best-suited for working with PDF files in Python. It can be used to extract text and metadata from PDF documents.

3. NLTK

As the name suggests, Natural Language Toolkit (NLTK) is perfect for natural language processing in Python. It provides tools for tokenization, stemming, tagging, and parsing.

4. Textract

Textract is a library used for extracting text from any document format. It can extract text from PDFs, images, and scanned documents.

5. Gensim

Gensim is a library used for topic modeling and document similarity analysis. It can be used to find similar documents based on their content.

6. Scikit-learn

Scikit-learn is a library used for machine learning in Python. It can be used to build classification, regression, and clustering models.

7. PyTesseract

PyTesseract is an optical character recognition (OCR) library in Python. It can be used to extract text from images and scanned documents.

8. PyMuPDF

PyMuPDF is another great library for working with PDF files in Python. It can be used to extract text, images, and metadata from PDF documents.

9. OpenCV

OpenCV is probably the best library out there for computer vision in Python. It can be used to perform image processing tasks such as image segmentation, object detection, and image recognition.

10. TensorFlow

TensorFlow is used for machine learning and deep learning in Python. It can be used to build models for image recognition, natural language processing, and other tasks.

These libraries can be used to build a variety of document AI solutions, including:-

1. Document classification
2. Information extraction
3. Entity recognition
4. Sentiment analysis
5. Document summarization
6. Topic modeling
7. Image recognition and OCR
8. Fraud detection

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You can build a powerful in-house document AI solution by combining these libraries with other Python tools and frameworks, such as Flask for building APIs and Docker for containerization.

However, you should know that building an in-house document ai solution would cost you multiple sleepless nights, and you'd be putting your efforts into something that doesn't do any good to your business directly.

Why Docsumo?

We at Docsumo, can automate document processing for your business saving you the trouble of doing everything manually. We not only make automation possible, but also ensure accuracy and adaptability. While we work on making our product even better, get in touch with us and sign up for a free trial now!