In the modern information age, extracting usable data from PDFs remains a stubborn bottleneck for businesses, researchers, and public institutions. Despite the proliferation of digital documents that hold scientific findings, regulatory records, and historical archives, turning those PDFs into structured, machine-readable data is still a complex and error-prone endeavor. The core difficulty lies in the very design of PDFs: they were born out of a print-oriented mindset, where layout and typography mattered more than long-term machine interpretability. As a result, many PDFs are effectively images of information, not straightforward text streams, which demands Optical Character Recognition (OCR) or equivalent technologies to convert them into usable data. The struggle spans practical, policy, and technical dimensions, affecting workflows from data curation in research to regulatory compliance in government agencies, and even the retrieval of reliable information for journalism.
The PDF data extraction challenge
For years, diverse sectors—governments, corporations, and research teams—have wrestled with the challenge of turning PDF content into digestible, actionable data. The problem is not merely about running OCR on a few pages; it is about maintaining context, preserving the original layout logic, and ensuring the semantic relationships between elements such as headers, tables, captions, and body text are accurately captured. Derek Willis, a lecturer in Data and Computational Journalism, has highlighted that PDFs are artifacts of a time when print layouts dictated software design. He notes that many PDFs are essentially pictures of information, which makes OCR indispensable to render the text usable for subsequent analysis. This situation becomes even more intricate when dealing with old documents, handwritten notes, or scanned pages with suboptimal image quality. The reliance on OCR is not optional in such cases—it is the gateway to any computational processing.
In the data landscape, vast quantities of information remain unstructured or semi-structured, resisting straightforward extraction. Studies suggest that a majority—roughly 80% to 90%—of the world’s organizational data exists in unstructured formats within documents. This sprawling reservoir of information is often locked behind two-column layouts, embedded tables, charts, or scanned pages that degrade the fidelity of text recognition. Accessing and leveraging this data is critical across multiple domains: digitizing scientific research so it can be re-analyzed, preserving fragile historical documents for future generations, streamlining customer service with accurate information retrieval, and ensuring that technical literature is accessible to AI systems used for analysis and decision-making. The scale of the challenge is not abstract; it translates to significant operational costs and efficiency losses in day-to-day workflows.
The consequences ripple across public services and private sectors alike. Willis emphasizes that the problem is especially acute for documents older than two decades, where the interplay of legacy formatting and archival scans amplifies extraction errors. The impact extends beyond the operational hurdles of courts, police, and social services by affecting journalists who rely on archival records for investigative reporting. Industries that depend on precise data, such as insurance and banking, are compelled to invest substantial time and resources to convert PDFs into data that can feed automated systems and analytics pipelines. The challenge is not merely about parsing text; it is about preserving fidelity to the original content, including numerical values, table structures, and the relationships between document components.
A concise historical thread runs through OCR’s evolution. From its early days to today’s sophisticated AI-driven approaches, the field has always balanced reliability with flexibility. Designers and data engineers have learned to anticipate predictable error modes, building post-processing checks and validation procedures to catch and correct mistakes. Yet, as documents grow more complex—with multi-column layouts, nested tables, and scanned pages of variable quality—the limitations of traditional OCR become more apparent. The result is a persistent demand for more advanced, more context-aware reading capabilities that can interpret not just text in isolation but also the visual and structural relationships that define meaning in a document.
A brief history of OCR
Optical Character Recognition technology traces its origins to the 1970s, when researchers began to transform images of printed text into machine-readable data. The field owes much of its early momentum to the innovations of Ray Kurzweil, whose work culminated in the Kurzweil Reading Machine in 1976. This device represented a milestone in accessibility technology for the visually impaired and served as a practical demonstration of early pattern-matching approaches to character recognition. Traditional OCR systems operate by analyzing images to detect light and dark pixel patterns, then matching those patterns to known character shapes. Once a match is identified, the system outputs the corresponding text.
These conventional OCR methods perform well on clean, well-formatted documents with standard fonts and minimal noise. However, they face significant challenges when confronted with unusual fonts, tightly spaced two-column layouts, embedded tables, or low-quality scans. The core limitation is that pattern-matching approaches are inherently brittle: small changes in font, spacing, or image degradation can lead to misrecognitions. As a result, traditional OCR produces predictable error modes—errors that practitioners can anticipate and correct through post-processing pipelines or manual review. This reliability has, paradoxically, preserved continued use of traditional OCR in many workflows where stability and explainability outrun the benefits of newer approaches.
Despite these limitations, traditional OCR remains pervasive because it provides deterministic behavior. In many cases, organizations design workflows around known error patterns, creating rules and heuristics that help recover the most critical data. Yet, with the growing demand for processing speed, scale, and richer data capture (such as handwriting or complex diagrams), traditional OCR started giving way to more ambitious, AI-based strategies. The rise of transformer-based large language models (LLMs) and other AI systems has opened a new frontier: reading documents in a way that blends visual layout understanding with natural language understanding. This shift has redefined what “reading” a document means in the context of automated data extraction.
The rise of AI language models in OCR
Moving beyond rigid, character-by-character recognition, modern approaches leverage multimodal AI models that can process both text and visual information. Multimodal large language models are trained on data where text and images are integrated into representative tokens that the system can reason about within a powerful neural network. In practice, these models can analyze a page holistically, considering how elements relate to one another in space, while also interpreting textual content. This dual capability enables the models to infer the relationships between visual features—such as headers, captions, and body text—and to interpret complex layouts, tables, and charts with a level of contextual awareness that traditional OCR often lacks.
A widely cited example of this paradigm is the way how vision-enabled LLMs handle documents like PDFs: they can, in principle, understand the overall structure of a page, the sequencing of sections, and the navigational cues that guide a reader. When a user uploads a PDF to a system powered by a vision-capable LLM, the model processes both the appearance of the document and its textual content to generate a structured representation. This approach represents a departure from conventional OCR, which tends to focus on isolated text recognition and sequence alignment, and it promises a more comprehensive extraction of meaningful data from complex documents.
Industry observers have noted that not all LLMs perform equally well on these tasks. A critical insight is that the performance of document-reading AI depends heavily on the model’s architecture and training data, as well as the size of its context window—the amount of content the model can consider at once. In practice, this means that some models can process lengthy documents by breaking them into manageable chunks and reassembling a coherent interpretation, while others struggle with large-scale content, especially when dealing with handwritten material or highly irregular layouts. The differences between models translate into real-world outcomes: higher accuracy, fewer corrections, and faster throughput can distinguish a practical tool from a theoretical capability.
Analysts highlight that a key advantage of vision-capable LLMs over traditional OCR is the broader context they can apply when predicting characters or interpreting data. For instance, a digit recognition task—distinguishing between a 3 and an 8—may benefit from contextual cues beyond a single line of text. In practice, this means that LLMs can leverage surrounding information to improve digit accuracy, reducing certain types of misreadings that would plague pattern-matching OCR. Willis, who has tested and evaluated several solutions, notes that while traditional OCR tools like Amazon Textract remain strong in specific scenarios, they are constrained by their internal rules and limits on how much text they can reference. By contrast, LLM-based approaches offer an expanded context, which can translate into better predictions in challenging cases.
The shift toward LLM-based OCR is not merely about achieving higher accuracy in isolation; it also reflects a desire to handle real-world documents with more diverse formats. These models claim to excel at interpreting complex layouts, discerning between headers, captions, and body text, and overall improving end-to-end data extraction. This holistic capability is what enables them to potentially outperform traditional OCR in scenarios where data extraction must preserve nuanced document semantics. Nevertheless, the implementation of LLM-based OCR is not without caveats, and practitioners must weigh the benefits against new challenges inherent to probabilistic language models.
New attempts at LLM-based OCR
As demand for more capable document-processing solutions grows, new players have entered the field with specialized offerings. One notable entrant is a French AI company known for its smaller language models. This firm launched an OCR-focused API designed to extract text and images from documents that feature complex layouts. The claim is that the system uses its language model abilities to understand and process the various elements of documents, thereby enabling more accurate extraction in difficult cases. In practice, however, field tests have shown a mixed performance that underscores the gap between marketing claims and real-world results.
Feedback from practitioners who test these new OCR-oriented models is telling. In one cited instance, a participant attempted to parse a table from an old document with a complex layout. The new OCR-specific model produced by the entrant repeatedly returned incorrect outputs, such as repeating city names while misrepresenting numerical values. Another observer flagged a handwriting-related limitation, noting that the model struggled with handwriting and tended to hallucinate or misinterpret writers’ strokes. These experiences illustrate a common pattern: performance in constrained, idealized prompts can diverge significantly from performance in real-world, messy documents.
Competitors and observers generally regard Google as a leading force in the space, particularly with its vision-enabled, context-aware AI systems. Among the models tested, Google’s Gemini 2.0 Flash Pro Experimental stood out for its ability to handle PDFs better than some rivals in complex cases. In practical testing, Gemini demonstrated fewer mistakes on difficult documents, including those containing handwritten notes, when compared with other recent models. A key factor behind Gemini’s performance is its broader context window, which allows the model to process larger documents in segments while maintaining coherence across sections. This capability reduces fragmentation and enables more robust interpretation of long documents that would overwhelm narrower systems.
Context length, or the context window, emerges as a pivotal advantage. The capacity to upload large documents and analyze them piece by piece, while preserving continuity, enables more accurate extraction and interpretation. The improved handling of handwritten content by certain Gemini iterations further strengthens the argument that context-aware, vision-enabled LLMs may hold a practical edge for real-world document processing. Still, the landscape remains dynamic, with ongoing experimentation and benchmarking across multiple models and use cases.
The drawbacks of LLM-based OCR
Despite the promise, LLM-based OCR introduces a suite of challenges that practitioners must address before adopting these tools at scale. One central concern is the probabilistic nature of these models: they generate outputs based on pattern recognition and statistical likelihood, which means they can produce plausible but incorrect results—hallucinations that look convincing but are not grounded in the source data. This risk is particularly acute in sensitive domains where accuracy matters deeply, such as financial statements, legal documents, or medical records, where even small errors can have outsized consequences.
Another significant risk is prompt-following behavior: LLMs can sometimes interpret text as instructions and follow them in ways that may not reflect the user’s intent. This phenomenon, sometimes described as accidental instruction following, opens the door to potentially harmful outputs if the model misinterprets the surrounding content as an authoritative directive. Such issues are compounded by the possibility of misinterpreting tables or misaligning headings with data rows, which can produce outputs that appear coherent but are fundamentally wrong. The risk of misinterpretation extends from simple typographical misreads to more dangerous misalignments that corrupt data relationships and yield misleading conclusions.
The handwriting challenge is particularly troublesome. When text is illegible or highly stylized, models may invent content, substituting plausible but invented text for the missing data. This “text hallucination” undermines trust and makes it risky to rely on automated extraction without human oversight. In fields where precise data is essential—financial statements, legal filings, or clinical records—these risks necessitate rigorous quality control measures, multi-step review processes, and conservative deployment strategies. Human oversight remains a critical guardrail to ensure data integrity and to catch errors that automated systems might miss.
Prominent voices in the field have underscored the gravity of these issues. Experts like Simon Willison have highlighted the risk of accidental instruction following and the harmful consequences of table interpretation errors. He has recounted scenarios where vision LLMs could misassociate data lines with incorrect headers, producing output that seems plausible but is completely junk when scrutinized. The combined risk of misreading, mislabeling, and hallucination makes fully automated, high-stakes data extraction problematic without substantial human validation and verification steps.
The reliability question is not academic; it affects business processes and critical decision-making. Financial statements, regulatory filings, or medical records require a degree of precision that may not be safely achieved through current automation alone. These limitations encourage a measured approach: deploy LLM-based OCR selectively in non-critical contexts, pair automated extraction with robust data validation workflows, and maintain human-in-the-loop processes for high-stakes data. This balanced approach acknowledges the capabilities of modern OCR while recognizing its boundaries.
The path forward
Despite the limitations, progress in OCR and document processing continues at a rapid pace. Even in an era of highly capable AI systems, there is no single, perfectly reliable solution for every document type. The race to unlock data from PDFs has intensified as companies seek context-aware, generative AI tools capable of reading and interpreting multilingual, multi-layout documents. Some motivations behind these efforts are strategic and practical: AI developers want access to diverse training data, while researchers and historians aim to unlock archival content for new analyses. As Willis observes, the push to extract information from documents arises, in part, from the potential to leverage this material for training data and model improvements. Documents of various formats, not just PDFs, present both an opportunity and a challenge for future AI systems.
The evolving landscape suggests a multi-pronged strategy. First, continued refinement of vision-enabled LLMs and related multimodal architectures will likely yield better accuracy and resilience when dealing with complex layouts and handwriting. Improvements in model training, data curation, and architectural innovations—especially those that enhance handling of scale, context, and robustness—will contribute to more reliable document reading capabilities. Second, improvements in pre- and post-processing pipelines will help mitigate errors, with emphasis on validating extracted data, reconciling layout semantics, and detecting anomalies that require human review. Third, hybrid approaches that combine the strengths of traditional OCR methods with modern AI models may offer practical pathways for reliable extraction across a broad range of document types. By balancing deterministic, rule-based recognition with probabilistic, context-aware reasoning, organizations can tailor solutions to their specific data needs.
Another factor shaping adoption is the practical availability of tools and platforms. Industry leaders are offering context-aware, document-processing products that integrate OCR capabilities with broader AI pipelines. The competitive landscape also reflects strategic interests in data access: corporations and researchers alike recognize that documents contain valuable knowledge, and more capable OCR tools can unlock that knowledge for downstream analytics, decision support, and automated workflows. For some players, access to training data from processing large volumes of documents is a core strategic asset that can accelerate model improvements and create a virtuous cycle of performance gains.
The potential impact of improved OCR and AI-driven document reading spans multiple stakeholders. Historians and archivists could digitize vast repositories of documents to facilitate research, trend analysis, and broader public access to historical records. Businesses could expedite compliance, risk assessment, and operational reporting by turning unstructured PDFs into structured, queryable data. Journalists could leverage enhanced document-reading capabilities to uncover insights from long timelines of records, court documents, and technical reports. As with any powerful technology, however, there is a need for careful governance, transparency about limitations, and robust safeguards against misinformation, data leakage, and biased outcomes.
The broader implications include considerations about data privacy and ethical usage. The deployment of more capable OCR systems must align with privacy guidelines and data protection standards, particularly when processing sensitive information such as financial data, medical records, or personal identifiers. Organizations should implement rigorous access controls, auditing, and data sanitization practices to prevent exposure of confidential material. At the same time, practitioners must communicate the limitations of automated extraction clearly to users and stakeholders, to ensure that decisions are not based on flawed or incomplete data.
In summary, the OCR landscape is shifting from traditional, pattern-based recognition toward sophisticated, context-aware AI systems that can read documents with a level of understanding closer to human reasoning. The promise is substantial: faster data extraction, broader coverage of document types, and richer, more usable data streams for analytics and AI applications. The risks—hallucinations, misinterpretations, and accidental instruction following—are real and non-trivial, but they can be managed through careful deployment, rigorous validation, and ongoing model refinement. As more organizations invest in context-aware OCR solutions and as model architectures continue to evolve, the potential to unlock the wealth of information trapped in PDFs and other documents grows ever stronger. The coming years are likely to witness a dynamic convergence of traditional OCR reliability with AI-driven interpretive power, yielding workflows that are both efficient and more capable of preserving the integrity of source material.
Conclusion
The journey of OCR—from its early, rule-bound roots to today’s context-aware, vision-enabled AI systems—highlights a persistent tension between reliability and capability. PDFs, surveys of tabular data, two-column layouts, and handwritten notes collectively test the limits of what automated reading can accomplish. While LLM-based OCR has demonstrated notable advantages in understanding complex layouts and processing large documents, it also introduces risks that require thoughtful governance, validation, and human oversight. The path forward likely involves a blend of traditional OCR strengths with AI-driven context and multimodal processing, combined with robust data-quality practices. As researchers and practitioners continue to refine models and workflows, the potential to transform vast reservoirs of unstructured information into accessible, actionable data grows closer to realization. The ultimate outcome will depend on how well the industry navigates accuracy, reliability, and responsible deployment in real-world, high-stakes contexts.