Untangling the Complexities of PDF Extraction
Mar 15, 2024
Gradient Team
Accelerator Block for PDF Extraction
With the launch of our Accelerator Block for PDF extraction, we enabled users to easily and effectively extract text or data from PDFs to support various business and operational workflows. Unstructured PDFs are easily converted into text and structured data, which can then be used to enable subsequent operational tasks (e.g. document summarization, KYC compliance, etc).
However the process that it took to get here is no small feat. PDF extraction can be notably challenging due to several inherent characteristics of the PDF format and the nature of the data it often contains. Today we’re diving into those challenge and how we designed our Accelerator Block for PDF Extraction to overcome those challenges.
Understanding the Challenges
Layout and Structure
Inconsistent Layout: Documents as PDFs often have inconsistent layout, which means that the content is positioned in a nondeterministic manner, making it hard to programmatically extract text, tables, or images in a way that retains the original meaning or structure.
Lack of Structural Metadata: PDFs frequently lack explicit structural metadata, such as headings, paragraphs, or the semantics of tables. This absence complicates the identification of different content types and the understanding of their hierarchy and interrelationships.
Complex Layouts and Formats: Documents with multi-column layouts, footnotes, sidebars, or tables pose additional challenges. Typically, extraction tools may strip away context during the process, diminishing the usefulness of the data retrieved.
Content Quality and Types
Quality Issues: When dealing with scanned PDFs, the quality of the scan can significantly affect the ability to extract text. Poor quality scans may result in OCR errors, missing characters, or incorrect text, requiring manual correction or sophisticated error-handling algorithms.
Multimodal Content: PDFs can contain a mix of text, images, graphics, and sometimes even multimedia elements. Extracting data from these varied content types requires different approaches and technologies, complicating the extraction process.
Security and Encryption
Security Features & Encrypted Content: Some PDFs contain embedded or encrypted content, which can require additional steps to access and decode before extraction is possible. PDFs can have security settings that restrict copying, printing, or editing of the document. These features can prevent extraction tools from accessing the content unless appropriate permissions are provided or bypassed.
Overcoming the Challenges
Each Accelerator Block is powered by custom Gradient LLMs that are fine-tuned to maximize performance across each task. In order to overcome the challenges that most PDF extraction tools experience, our PDF extraction pipeline consists of multiple small models each optimized for a specific part of the process.
Layout and Text
Gradient uses a combination of PDF metadata and state of the art vision models to understand the structure and layout of a PDF. PDF metadata, when exists, is helpful to provide baseline information about the content structure. The vision model is then layered on to identify more complex types of data (e.g. titles, section headers, footers, citations) and understand text hierarchy.
Additionally, Gradient applies a multimodal model to enable character-level identification and use affinity between the characters to parse out words in higher fidelity from lower quality PDFs. This technique is inspired by previous scene text detection methods.
Tables
Tables pose a unique challenge given that much of its value is in the specific structure, not just the text content, and the tabular structure is not explicitly provided in any underlying PDF metadata. Many other PDF extraction tools on the market parse table content as plain text in an unpredictable manner, resulting in loss of all row / column associations.
Gradient fine-tuned two small models specifically to increase the accuracy of extracting tables out of PDFs.
Table detection model – Identifies bounding boxes of tables within a document. This allows the pipeline to single out tables to go through specific subsequent steps.
Table structure model – Identifies bounding boxes for table headers, rows, and columns within an identified table.
Once the bounding boxes for each cell is identified, further algorithms are implemented to associate the rows and columns correctly.
Post-Processing
Oftentimes, minor artifacts may show up as a result of the PDF extraction process, such as duplicate punctuation or common OCR spelling errors. The Gradient PDF extraction pipeline leverages an LLM to clean up these artifacts before returning the final content back to the user.