By super.AI
Modern organizational workflows heavily depend on searchable documents, which commonly contain tables that organize valuable information clearly and concisely (from a human’s perspective, anyway). Documents themselves vary in type (e.g., invoices, receipts, insurance documents, bills of lading, bank statements, and more), and the tables they include also vary (different row and column labels, counts, etc.).
Although humans can quickly discern different document types using context clues, and easily make sense of tables regardless of structure and formatting, machines aren’t so capable. This makes automating data extraction from complex documents, and doubly so for information within them presented in tables, a challenging task for most document extraction solutions.
This article offers a deep dive into automating the extraction of tabular data from complex documents, scanned images, and other sources of valuable business information.
Tables are fairly ubiquitous in the world of business data. However, there are several places they are encountered most when attempting to automate data extraction:
Manually copying and pasting data from documents is an inefficient process as it may result in table structure alteration, making it challenging to bring the data back to its original organized form. Manual extraction processes require significant verification and reformatting, which can be time-consuming and susceptible to errors.
Businesses are constantly looking for ways to convert their documents, particularly those with abundant tabular data, into editable table formats such as Excel or CSV. Additionally, they seek methods to make their data searchable, thus facilitating the process of finding and extracting relevant information.
Automation tools can extract tables from various documents, such as PDFs, Word documents, scanned images, and HTML pages. Some common features of these tools include the ability to handle multiple file formats, support for batch processing of multiple documents, and the ability to export the extracted data in various file formats such as CSV or Excel. However, the quality of the output can vary depending on the quality of the input document and the specific tool being used.
Some automation tools that are used to extract tables from various kinds of sources include,
Automating the extraction of tables from PDFs and other sources has a plethora of benefits for businesses, which include extracting legacy data stored in a tabular format; digitizing information to streamline processes and improve data reliability; collecting and organizing invoice and form data more efficiently; and reducing the risk of data misplacement or inconsistencies. Some specific end-use cases for automated table extraction include:
The challenges for legacy OCR and traditional data extraction tools when it comes to extracting tables from PDFs and other document types can be bucketed into two groups:
Legacy OCR solutions struggle with the variety of structural layouts that tables can have, including different numbers of columns and rows, as well as different font sizes, colors, and styles. This makes it difficult for OCR algorithms to accurately identify and extract tabular data. Additional variables that can significantly hinder OCR performance include:
While tables are meant to present data in a clear and organized format, not all tables are created equal. Many different types of tables can be used to present data, each with its own strengths and weaknesses. The following is a list of some of the most common types of complex tables that OCR can have trouble extracting:
AI and ML tools can be used to circumvent many of the challenges faced by OCR and legacy algorithms in table extraction. One of the main challenges with OCR is that it can have difficulty accurately recognizing tables with complex structures, such as nested tables or tables with handwriting. AI and ML algorithms can be used to analyze the table's structure and identify the data's location within it, even in cases where the table is not well structured or includes handwritten text.
Another challenge that OCR can face is the ability to accurately extract data from tables that are presented in different languages or with different font styles and sizes. By using natural language processing (NLP) techniques and machine learning models, AI and ML tools can be trained to recognize and extract text from tables regardless of language or styling.
Unlike OCR and legacy computer vision algorithms that only recognize visual similarities between pixels and characters, AI tools can understand the context of the data, and make distinctions about what data is relevant or "makes sense." Artificial intelligence and machine learning techniques can be used to train models to understand data in context, which can help improve the accuracy of table extraction. For example, by using NLP and ML to understand the meaning of the text within the table and the relationships between the data, the model can better understand the tabular structure and improve the accuracy of the extraction process.
Super.AI’s Intelligent Document Processing (IDP) solution was built on top of our unified AI platform that can process any document or unstructured data type. The obstacles that OCR and other traditional data extraction solutions struggle with are the exact type of challenges our technology excels at. Rather than approach complex data processing tasks from a single angle, we break each task down into smaller parts, then leverage the best AI, human, or software worker for each component. When it comes to extracting tables from PDFs, our solution leverages the most effective and current AI models for pre-processing and extraction, then combines the results into a unified output.
Super.AI intentionally avoids proprietary OCR and AI models, and instead tests and adopts the most performant combination of tools for a given scenario. Additionally, our Data Processing Crowd, a high-quality, on-demand resource pool for data labeling, post-processing, and exception handling makes it possible to leverage trained human workers to quickly process or correct tables machines may have trouble understanding. Each human input is used to continuously train models that can quickly learn new table formats to rapidly improve automation rates.
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