Deep learning and Optical Character Recognition (OCR) have combined forces to form a cutting-edge AI solution for capturing data from complex tables. Even though manual extraction is still an effective form of data capture, AI algorithms can emulate human comprehension and extract information from tables with complete accuracy.
In this article, we’ll cover exactly how AI can extract from tables, how businesses can harness this technology and how to make it work for your business.
Complex tables can appear in all financial documents —such as invoices and financial statements. Often, the value of that data is high—with organisational decision-making hinging on fast and accurate data extraction.
Legacy technologies like OCR struggle to extract specific information from tables. Although these older technologies can convert data on PDFs into machine-readable text, they can’t contextualise or understand the meaning of the text.
More importantly, they can’t learn from their mistakes either. If OCR engines repeatedly make a mistake – such as combining data from adjacent cells – retraining them would be extremely difficult.
Conversely, AI uses the same OCR technology to convert a PDF to machine-readable text, yet it enjoys the flexibility to learn from its mistakes instantly.
The AI recognises the page's structure as a table, employing machine-learning algorithms to discern meaningful patterns within the data while considering the context of each data point. These algorithms parse complex tables, comprehending intricate relationships between cells, headers and the surrounding content.
The extracted data then undergoes rigorous validation, where the AI promptly identifies and flags any errors. Finally, the AI meticulously organises the data before outputting it.
AI technology can extract from any media, including images, audio and video files. Consequently, PDFs of complex tables are no match for AI. Although there is no limit to what AI can extract from, some models may require training. Training the model consists of uploading examples of complex tables for the AI model to process. Generally, this will take approximately two days.
To learn more about the training process, contact a top-tier AI vendor for a demo.
QA is non-negotiable when it comes to addressing the unique challenges posed by complex formatting and table structures. Constant QA checks will ensure that the AI technology returns your data quickly and accurately.
Let’s take a closer look at five examples of high-quality QA.
AI technology must manage empty or merged cells, preventing errors from infiltrating the outputted dataset.
AI models can harness external datasets (such as Companies House) to cross-reference and enrich extracted data.
Any errors in the extracted data must be flagged for either manual review or re-review by the AI.
Full auditing will increase confidence in the captured data. From its initial capture to any corrective actions, careful documentation will increase the AI model’s performance.
Feedback from the AI model should improve its performance. Examples of these improvements include updating extraction rules or enhancing preprocessing steps.
Today’s AI models can provide complete accuracy in captured data. However, what makes an outstanding AI data extraction solution is QA and customer service. If you encounter any issues with your model, it’s important that the vendor can quickly resolve them.
One of the major disadvantages of manual data extraction is that highly-qualified employees often spend unnecessary time poring over PDFs of complex tables to find specific data. In contrast, AI data capture software will output relevant data from tables into organised spreadsheets (in any preferred format, including Excel, JSON, CSV and more).
In summary, the extraction process will work in the background, outputting actionable data.
Dun & Bradstreet provides analytics and insights for global firms. Every year, they receive millions of information statements and annual reports that contain complex multi-page tables, handwriting and other challenging elements.
Evolution AI’s proprietary AI technology provided them with a real-time feed of information from Companies House. In addition to accuracy in the captured data, Evolution AI delivered exceptional customer support throughout the deployment process. To discover more about Dun & Bradstreet’s journey with Evolution AI, please see our case study.
Extracting complex data shouldn’t be a difficult process. Evolution AI has set a new standard in automated data extraction. Our award-winning data extraction tool is designed to work on any financial document, reducing costs and improving data quality.
For further information about how Evolution AI’s award-winning AI data extraction software can revolutionise and scale your business, we invite you to book a demo with one of our experts. Alternatively, for any queries, feel free to email them to hello@evolution.ai, and a member of our team will contact you.
Deep learning and Optical Character Recognition (OCR) have combined forces to form a cutting-edge generative AI solution for capturing data from complex tables. Even though manual extraction is still an effective form of data capture, AI algorithms can emulate human comprehension and extract information from tables with complete accuracy.
In this article, we’ll cover exactly how AI can extract from tables, how businesses can harness this technology and how to make it work for your business.
Complex tables can appear in all financial documents—such as invoices and financial statements. Often, the value of that data is high—with organisational decision-making hinging on fast and accurate data extraction.
Legacy technologies like OCR struggle to extract specific information from tables. Although these older technologies can convert data on PDFs into machine-readable text, they can’t contextualise or understand the meaning of the text.
More importantly, they can’t learn from their mistakes either. If OCR engines repeatedly make a mistake – such as combining data from adjacent cells – retraining them would be extremely difficult.
Conversely, AI uses the same OCR technology to convert a PDF to machine-readable text, yet it enjoys the flexibility to learn from its mistakes instantly.
The AI recognises the page's structure as a table, employing machine-learning algorithms to discern meaningful patterns within the data while considering the context of each data point. These algorithms parse complex tables, comprehending intricate relationships between cells, headers and the surrounding content.
The extracted data then undergoes rigorous validation, where the AI promptly identifies and flags any errors. Finally, the AI meticulously organises the data before outputting it.
AI technology can extract from any media, including images, audio and video files. Consequently, PDFs of complex tables are no match for AI. Although there is no limit to what AI can extract from, some models may require training. Training the model consists of uploading examples of complex tables for the AI model to process. Generally, this will take approximately two days.
To learn more about the training process, contact a top-tier AI vendor for a demo.
QA is non-negotiable when it comes to addressing the unique challenges posed by complex formatting and table structures. Constant QA checks will ensure that the AI technology returns your data quickly and accurately.
Let’s take a closer look at five examples of QA.
AI technology must manage empty or merged cells, preventing errors from infiltrating the outputted dataset.
AI models can harness external datasets (such as Companies House) to cross-reference and enrich extracted data.
Any errors in the extracted data must be flagged for either manual review or re-review by the AI.
Full auditing will increase confidence in the captured data. From its initial capture to any corrective actions, careful documentation will increase the AI model’s performance.
Feedback from the AI model should improve its performance. Examples of these improvements include updating extraction rules or enhancing preprocessing steps.
Today’s AI models can provide complete accuracy in captured data. However, what makes an outstanding AI data extraction solution is QA and customer service. If you encounter any issues with your model, it’s important that the vendor can quickly resolve them.
One of the major disadvantages of manual data extraction is that highly-qualified employees often spend unnecessary time poring over PDFs of complex tables to find specific data. In contrast, AI data capture software will output relevant data from tables into organised spreadsheets (in any preferred format, including Excel, JSON, CSV and more).
In summary, the extraction process will work in the background, outputting actionable data.
Dun & Bradstreet provides analytics and insights for global firms. Every year, they receive millions of information statements and annual reports that contain complex multi-page tables, handwriting and other challenging elements.
Evolution AI’s proprietary AI technology provided them with a real-time feed of information from Companies House. In addition to accuracy in the captured data, Evolution AI delivered exceptional customer support throughout the deployment process. To discover more about Dun & Bradstreet’s journey with Evolution AI, please see our case study.
Extracting complex data shouldn’t be a difficult process. Evolution AI has set a new standard in automated data extraction. Our award-winning data extraction tool is designed to work on any financial document, reducing costs and improving data quality.
For further information about how Evolution AI’s award-winning AI data extraction software can revolutionise and scale your business, we invite you to book a demo with one of our experts. Alternatively, for any queries, feel free to email them to hello@evolution.ai, and a member of our team will contact you.
