Upscaling Tabular Data Extraction With Intelligent Data Capture
by Amit Jnagal, on October 10, 2019 9:30:00 AM PDT
Whether qualitative, quantitative or temporal dataset, a tabular presentation has always been a systematic yet logical way to represent data. As data increases at unprecedented rates, the struggle lies in binding data-enriched tables to paper-centric sources. Also, the difficulty with these paper-centric documents is that it limits the capability to automatically extract and interpret insights computationally for future use.
As the volume of data increases, so does the complexity of the tables, making it difficult to understand. Conversely, manual data entry is slow and repetitive which can disengage an employee. In both cases, there is a greater chance for error which could snowball into larger problems. Additional concerns related to manual re-keying of data includes:
• Inconsistency in data entry, miskeying information
• Lack of security
• Duplication of data entry
• Losing a competitive edge
Transitioning from the Manual Method to Modern Data Extraction
Advances in digital technologies are driving more and more organizations to integrate them into their existing workflows. In this race, OCR technology attempts to address tabular data extraction. Surprisingly, the attempt could not succeed as it failed to address challenges such as:
• Identifying table
• Type of table (such as comparison reports or presentation reports)
• Variety of structural layouts and visual relationships
• Representation for visualization
• Variety of value presentation patterns
So, Infrrd came up with a unique approach. Our ‘Intelligent Data Capture’ (IDC) encapsulates advanced AI-enabled capabilities such as Machine Learning and NLP to trawl through reams of documents to detect, analyze, and classify tables. But it doesn't end there. Next, the data is captured in digital format, which routes data throughout the business environment for future purposes.
Infrrd’s machine learning capability can:
• Identify tables in the document (their outer boundaries)
• Segment the table to recognize and detect the inner boundaries of the table (i.e. the rows, columns, and individual table cells).
• Classify tables into different types (e.g. complex, long, folded, simple) using layout features.
• Extract table content
Also, the Natural Language Processing (NLP) techniques help interpret table content (such as table titles, footnotes and non-table prose discussing a table), as well as understand both cell content and its relationship to the table content. Integrating this platform into your business will reduce the time and risks of errors associated with manually rekeying datasets from tables, which will ultimately impact your service delivery in a positive way.
At times, due to the complexity and diversity of documents, implementing IDC technology can be challenging. However, consulting with the right partner will ensure you balance your data capture needs to get you the functionalities you need to maximize business outcomes.