Nov 18, 2025

From Scanned PDFs to Structured Data: Why Quality Matters in the Age of AI

In an increasingly data-driven world, the ability to transform raw information into actionable insights is paramount. For many organizations, particularly those dealing with historical archives, legal documents, or complex financial records, this journey often begins with scanned PDFs. The challenge, however, isn't merely converting these images into text; it's about ensuring the quality of that conversion to yield truly structured, usable data. This is where the critical importance of data quality, especially when moving From Scanned PDFs to Structured Data: Why Quality Matters, becomes undeniably clear. As we navigate the complexities of digital transformation, the promise of artificial intelligence (AI) and multimodal large language models (LLMs) offers unprecedented opportunities, but their success hinges entirely on the integrity of the data they process.

The Enduring Challenge of Historical Documents and Scanned Data

The digital age has brought with it an imperative to digitize vast troves of historical and archival materials. Yet, these efforts often encounter significant hurdles. Historical documents, whether they are 18th-century Ottoman Turkish manuscripts or German patents from 1877-1918, present a unique set of challenges that traditional digitization methods struggle to overcome (Source, Source).

These challenges include:

• Paleographically challenging contexts: The script itself can be difficult to decipher, especially in handwritten documents or those using archaic fonts like Gothic (Source, Source).
• Non-Latin scripts: Languages like Ottoman Turkish, written in a modified Perso-Arabic script, are digitally marginalized, making them inaccessible to modern Turkish speakers without specialized education and posing significant challenges for standard OCR (Source).
• Complex layouts: Documents arranged in double-column formats or containing a mix of Gothic and Roman fonts further complicate automated processing (Source).
• Paucity of original script transcriptions: While Latin-script transliterations might exist for some non-Latin historical documents, transcriptions in the original script are often scarce, hindering the development of robust Handwritten Text Recognition (HTR) models (Source).
• Script mixing and prompt noncompliance: Even advanced models can exhibit limitations such as mixing different scripts or failing to adhere strictly to prompt instructions (Source).

These issues are not confined to historical texts; they are prevalent across various sectors where legacy documents, often in PDF or image format, need to be converted into structured, searchable data. The inherent variability and imperfections of scanned documents—from varying image quality to inconsistent formatting—underscore the limitations of traditional Optical Character Recognition (OCR) approaches.

The Limitations of Traditional OCR

Traditional OCR systems have been the workhorse of document digitization for decades. Tools like Tesseract OCR (open-source) and commercial solutions like Transkribus have enabled the conversion of images into text files (Source). However, their effectiveness often wanes when confronted with the complexities of historical documents or poor-quality scans.

Traditional OCR primarily focuses on recognizing characters and words based on predefined patterns. This unimodal approach, relying solely on visual input, struggles with:

• Ambiguity: Distinguishing between similar-looking characters, especially in degraded or stylized fonts.
• Contextual understanding: Lacking the ability to infer meaning from surrounding text or document layout, leading to errors in transcription.
• Non-standard scripts: Poor performance on scripts for which they haven't been extensively trained, such as Perso-Arabic script in Ottoman Turkish manuscripts (Source).
• Layout complexity: Difficulty in accurately segmenting text from complex, multi-column layouts, as seen in historical German patents (Source).
• Post-correction burden: The output from traditional OCR often requires extensive manual post-correction, which is time-consuming and costly (Source).

These limitations highlight a significant gap: while traditional OCR can generate text, the quality of that text and its transformation into truly structured data often falls short, necessitating further, often manual, intervention.

The Paradigm Shift: Multimodal LLMs for Data Extraction

The advent of multimodal large language models (LLMs) marks a significant paradigm shift in how we approach the challenge of transforming scanned documents into structured data. Unlike traditional OCR, which is unimodal (processing only one type of information, like text or images), multimodal AI is designed to mimic human perception by ingesting and processing multiple data sources simultaneously—including video, still images, speech, sound, and text (Source, Source).

This capability allows multimodal LLMs to understand and analyze media content more like a human would, bridging the gap between text-based intelligence, visual understanding, and auditory perception (Source).

How Multimodal LLMs Enhance Data Quality

Multimodal LLMs, such as Google's Gemini 2.0 Flash, Gemini 2.5 Pro, and OpenAI's GPT-4o, leverage their ability to combine visual and linguistic inputs to generate high-fidelity outputs, even in paleographically challenging contexts (Source, Source, Source).

The process typically involves three stages (Source):

1. Input Processing: Each input type (text, image, audio, video) is processed by a specialized model (Source).
2. Fusion (Shared Understanding Layer): The system converts all inputs into a shared internal representation, identifying relationships between text descriptions and visual elements, connecting audio cues, and weighting the importance of different modalities based on context. This creates unified embeddings that capture multi-sensory information (Source, Source).
3. Unified Output Generation: The AI connects information across formats to understand meaning and generates one coherent response in the appropriate format, such as text descriptions of visual content, generated images based on text prompts, or structured data extracted from mixed media documents (Source, Source).

This holistic processing allows AI to understand context, not just isolated data, leading to better understanding, natural interaction, and higher productivity (Source).

Real-World Impact: Historical Data Construction

The application of multimodal LLMs in historical data construction demonstrates their transformative potential. For instance, researchers leveraged Gemini-2.5-Pro and Gemini-2.5-Flash-Lite to construct a dataset of 306,070 German patents (1877-1918) from 9,562 archival image scans (Source).

The benchmarking exercise provided tentative evidence that multimodal LLMs could create higher quality datasets than research assistants, while also being more than 795 times faster and 205 times cheaper in constructing the patent dataset from the image corpus (Source). This suggests that multimodal LLMs are a "paradigm shift" in how datasets are constructed in economic history (Source).

Another project explored the use of Gemini 2.0 Flash for transcribing Ottoman Turkish manuscripts in their original Perso-Arabic script and augmenting existing data for training purposes (Source). The results showed that multi-modal prompting significantly improved transcription quality in paleographically challenging contexts, enabling a scalable method for producing Arabic-script transcription data from widely available Latin-script editions (Source). This novel, resource-efficient direction is crucial for building HTR datasets and systems for historical, non-Latin-script documents (Source).

The GitHub repository for benchmarking historical dataset construction using multimodal LLMs further illustrates this, comparing traditional OCR (Tesseract, Transkribus) with multimodal LLMs (GPT-4o, Gemini-2.0-flash) for OCR, OCR post-correction, and named entity recognition (Source). This pipeline is designed to extract structured data in CSV format from historical documents, benchmarking against ground truth TXT and CSV files using exact and fuzzy matching metrics (Source).

Multimodal AI vs. Traditional AI Models

The distinction between traditional and multimodal AI models is stark, particularly in their ability to handle diverse and complex inputs to produce high-quality structured data.

| Feature | Traditional AI Models | Multimodal AI Models