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Imagine sifting through mountains of paper documents in a bank—loan forms, invoices, reports—it's endless! Doing it manually takes time and can lead to errors. That's where automatic document processing comes in! Document processing models are designed to understand, interpret, and extract valuable information from a myriad of document types. To build such models, the training datasets play a pivotal role, providing the necessary variety and complexity needed for the models to generalize effectively.
Just like training data helps machines recognize different animals, AI models need tons of document examples to learn the domain specific language and structure. With this training, they can accurately identify key details like income figures or risk factors, making them superheroes for streamlining processes and bringing efficiency to the world of document processing!
Let’s discuss some of the datasets and their purposes!
Invoice Training Dataset for Document processing
Purpose
Invoice datasets are used to train AI models for automating the processing of invoices. This includes tasks such as the extraction of key information (vendor details, invoice amount, date), categorization of expenses, and validation against predefined criteria.
Beyond automating invoice processing, AI models trained on invoice datasets play a pivotal role in streamlining accounting workflows. They facilitate the extraction of crucial financial information, helping organizations maintain accurate records and expedite payment processes. Additionally, these datasets empower AI models to learn from a spectrum of scenarios, enhancing their adaptability to the ever-evolving landscape of invoicing practices.
Contents
An exhaustive invoice dataset not only spans various industries and businesses but also captures the dynamic nature of invoice layouts, including diverse formatting styles, languages, and layouts. This diversity ensures that AI models are well-equipped to handle the intricacies of invoices from different sources, fostering a high degree of generalization.
Challenges
Invoice datasets may pose challenges due to variations in document layouts, different languages, and the presence of unstructured data. Therefore, a comprehensive dataset is essential to train models that can generalize well across different scenarios.
Bank Statement Training Dataset for Document processing
Purpose
Bank statement datasets are crucial for developing AI models that focus on tasks such as transaction categorization, anomaly detection, and trend analysis. These models help in managing finances, budgeting, and identifying unusual activities.
Contents
A bank statement dataset includes a range of transactions, each with details like transaction date, description, amount, and currency. It should cover diverse financial activities, such as income, expenses, transfers, and investments, to ensure the model's versatility.
Challenges
Variability in statement formats, handling multiple currencies, and detecting irregular patterns are challenges that need to be addressed in bank statement datasets. Real-world datasets should reflect these complexities.
Receipt Dataset for Document processing
Purpose
Training AI models to extract information from receipts, including details like items purchased, prices, and dates.
Contents
Diverse receipts from different vendors capture variations in layout, language, and formatting.
Challenges
Receipt datasets present challenges in terms of diverse layouts, font sizes, and languages. Handling handwritten receipts and dealing with faded or distorted print are additional challenges that need to be addressed. A robust dataset should encompass these variations to ensure models can accurately extract information under real-world conditions.
Insurance Claim Forms Dataset for Document processing
Purpose
Developing models for automating the processing of insurance claims by extracting relevant information from claim forms, including policy number, date, terms and conditions, amount, etc.
Contents
A dataset comprising various insurance claim forms, covering different types of claims and including diverse structures and formats.
Challenges
Insurance claim form datasets introduce challenges related to varying layouts, terminology, and the inclusion of hand-written information. The complexity of medical jargon and the need to distinguish between different types of claims require a dataset that captures these intricacies, ensuring models can effectively process diverse insurance claim documents.
Medical Reports for Document processing
Purpose
Enhancing the capabilities of AI models to extract information from medical records, including patient details, diagnoses, and treatments.
Contents
De-identified medical records reflect different document structures and medical terminology.
Challenges
Medical record datasets pose challenges due to the sensitive nature of the information, varying formats across healthcare providers, and the inclusion of handwritten notes. Ensuring privacy and addressing the diversity of medical terminology are critical challenges that must be navigated in the creation of such datasets.
Human Resources Documents Dataset for Document Processing
Purpose
Enabling AI models to extract relevant information from resumes, job applications, and other HR-related documents.
Contents
A dataset comprising resumes, job applications, and HR documents with variations in formats, styles, and content.
Challenges
HR document datasets introduce challenges related to diverse resume formats, language variations, and the inclusion of unconventional information. The ability to handle creative resume layouts and accurately extract relevant details is crucial for the effectiveness of models trained on such datasets.
Real Estate Documents Dataset for Document Processing
Purpose
Supporting the extraction of information from real estate documents such as property deeds, leases, and contracts.
Contents
Documents from the real estate domain, covering various property-related transactions and agreements.
Challenges
Real estate document datasets pose challenges in handling complex legal language, diverse document structures, and variations in property-related terminology. Ensuring accuracy in extracting critical details from contracts, deeds, and leases is essential for the effectiveness of models trained on this dataset.
These are some examples of datasets for document processing. The key to successful document processing lies in having datasets that are representative of the diversity of documents encountered in real-world scenarios. These datasets help AI models generalize well and perform effectively across different document types and industries.
Collect Training Data for Document Processing
There are mainly two ways to collect training data for training an AI model for document processing.
Open Source Data
There are many open source datasets available that can be helpful to start the development of your AI model. You can find some datasets on Paperwithcode and Kaggle.
But open source data may not be as diverse as your model needs or may not have enough data. Also, open source datasets are mostly limited to the English language. And you may need to annotate the data. But it is always good to check for open source datasets.
The challenges with open source datasets can be overcome with custom collection of required documents.
Data Vendor
If you are looking for a huge volume with diversity, different domains, and datasets in multiple languages, then it is good to collaborate with data providers. A data provider can help in two ways;
They can provide real data as well as synthetic data. Although real data is the best for training a model, it is very difficult to collect the data. Collecting real data is more time consuming and costly than collecting synthetic data.
In our opinion, it is good to have a blend of real and synthetic data.
How can FutureBeeAI help?
We offer real and synthetic industry specific document training data collection services, as well as ready to use 200+ datasets for different industries. We offer printed as well as handwritten documents to train document processing models. We can also help you label and annotate your documents.
We have built some SOTA platforms that can be used to prepare the data with the help of our crowd community from different domains.
You can contact us for samples and platform reviews.
