Large Language Models Outperform Traditional HTR Software in Transcribing Handwritten Historical Documents

Core Concepts

Large language models (LLMs) demonstrate superior speed, cost-effectiveness, and accuracy compared to specialized HTR software in transcribing handwritten historical documents, especially when used for correction.

Abstract

• Bibliographic Information: Humphries, M., Leddy, L. C., Downton, Q., Legace, M., McConnell, J., Murray, I., & Spence, E. (2024). Unlocking the Archives: Large Language Models Achieve State-of-the-Art Performance on the Transcription of Handwritten Historical Documents. arXiv preprint arXiv:2411.03340v1.
• Research Objective: This paper investigates the efficacy of large language models (LLMs) in transcribing handwritten historical documents, comparing their performance to traditional Handwritten Text Recognition (HTR) software.
• Methodology: The researchers developed a software tool called "Transcription Pearl" that leverages commercially available LLMs (GPT-4o, Claude Sonnet-3.5, and Gemini 1.5-Pro) to transcribe and correct batches of handwritten documents. They tested these models on a corpus of 50 pages of 18th/19th century English handwritten documents, comparing their performance to Transkribus, a popular HTR software. Two error metrics, strict and modified Character Error Rate (CER) and Word Error Rate (WER), were used to evaluate the accuracy of the transcriptions.
• Key Findings: LLMs outperformed Transkribus in both speed and cost-effectiveness. Claude Sonnet-3.5 achieved the highest accuracy among the tested LLMs, with a strict CER of 7.3% and WER of 15.9% on out-of-the-box transcription tasks. When used for correction, LLMs further reduced error rates, with Claude Sonnet-3.5 achieving a modified CER of 4.1% and WER of 7.0% when correcting Gemini transcriptions. Notably, LLMs exhibited limitations in self-correcting their own outputs.
• Main Conclusions: The study demonstrates the potential of LLMs as a viable alternative to traditional HTR software for transcribing handwritten historical documents. Their superior speed, cost-effectiveness, and accuracy, particularly in correction tasks, position them as valuable tools for historians and archivists.
• Significance: This research significantly contributes to the field of Digital Humanities by introducing a novel approach to HTR using LLMs. The findings have the potential to revolutionize the digitization and accessibility of historical archives.
• Limitations and Future Research: The study acknowledges the limited size and scope of the testing dataset, focusing solely on English language documents from a specific period. Future research could explore the performance of LLMs on a wider range of languages, scripts, and document types. Further investigation into prompting techniques and hyperparameter optimization could potentially enhance LLM performance in HTR tasks.

Stats

LLMs achieved Character Error Rates (CER) of 5.7 to 7% and Word Error Rates (WER) of 8.9 to 15.9% on a corpus of 18th/19th century English language handwritten documents.
LLMs showed improvements of 14% and 32% respectively in CER and WER over specialized state-of-the-art HTR software like Transkribus.
When used for correction, LLMs achieved near-human levels of accuracy, with CERs as low as 1.8% and WERs of 3.5%.
LLMs completed transcription tasks 50 times faster and at approximately 1/50th the cost of proprietary HTR programs.
Claude Sonnet-3.5 achieved a modified CER of 5.7% and modified WER of 8.9% on transcription, correctly transcribing more than 91% of the words.
When correcting Gemini transcriptions, Claude Sonnet-3.5 achieved a strict CER of 5.7% and WER of 13.8%.
Claude Sonnet-3.5 corrected Gemini transcriptions to a modified CER of 4.1% and WER of 7.0%, representing reductions of 63% and 38% respectively in the initial modified error rates.

Quotes

"These results demonstrate that when LLMs are incorporated into software tools like Transcription Pearl, they provide an accessible, fast, and highly accurate method for mass transcription of historical handwritten documents, significantly streamlining the digitization process."
"These results demonstrate that frontier LLMs can achieve state-of-the-art performance without fine-tuning or training on specific document formats or handwriting styles."
"Unlike conventional HTR models that can transcribe text but are unable to correct it, we also found that frontier model LLMs could also be employed to significantly improve error rates by comparing images of the original handwritten pages to the text of LLM generated transcriptions to produce new, corrected transcripts."

Key Insights Distilled From

Unlocking the Archives: Using Large Language Models to Transcribe Handwritten Historical Documents

by Mark Humphri... at arxiv.org 11-07-2024

https://arxiv.org/pdf/2411.03340.pdf

Deeper Inquiries

How might the use of LLMs for HTR impact the accessibility and analysis of historical documents for researchers and the general public?

The use of LLMs for Handwritten Text Recognition (HTR) holds the potential to revolutionize the accessibility and analysis of historical documents for both researchers and the general public in several key ways:

Democratizing Access to History: LLMs can significantly reduce the time and cost associated with transcribing handwritten documents. This can be particularly impactful for smaller archives, libraries, and individual researchers who may not have the resources for large-scale digitization and transcription projects. Making these materials searchable and accessible online allows a wider audience, including those with disabilities, to engage with historical sources.

Accelerated Research: By quickly converting handwritten text to searchable digital formats, LLMs empower historians to analyze significantly larger datasets than was previously feasible. This can lead to new insights, discoveries, and a more nuanced understanding of the past. Researchers can use digital tools to perform text mining, network analysis, and other computational methods to uncover hidden patterns and connections within historical documents.

Enhanced Discoverability: With full-text search capabilities, researchers and the public can more easily locate relevant documents within vast archives. This can be particularly useful for finding information within collections that may not be fully cataloged or indexed. LLMs can also be used to translate historical documents, further breaking down language barriers and broadening access to global archives.

Preservation and Conservation: Digitizing fragile and deteriorating documents helps preserve them for future generations. LLMs can accelerate this process, ensuring that valuable historical records are not lost due to damage or decay. Digital surrogates can also reduce the handling of original documents, minimizing wear and tear.
However, it's crucial to acknowledge that LLMs are not a silver bullet. Challenges remain in ensuring accuracy, addressing biases in training data, and developing standardized practices for transcription and emendation.

Could the reliance on LLMs for HTR create biases in the interpretation of historical documents, particularly those with ambiguous handwriting or language?

Yes, the reliance on LLMs for HTR could introduce biases in the interpretation of historical documents, especially those with ambiguous handwriting or language. Here's why:

Bias in Training Data: LLMs are trained on massive datasets of text and code, which may contain historical biases reflecting the prejudices and perspectives of the time periods they represent. If these biases are not carefully addressed during training and fine-tuning, the LLM may reproduce them in its transcriptions, potentially leading to misinterpretations of the original text.

Difficulty with Ambiguity: Handwriting is inherently variable, and historical documents often contain idiosyncrasies, abbreviations, and faded ink that can be challenging even for human experts to decipher. While LLMs are becoming increasingly adept at handling ambiguity, they may still misinterpret characters or words, particularly in cases where contextual clues are limited.

Overconfidence in Output: The "fluency" of LLM-generated text can sometimes mask underlying errors or uncertainties. Researchers and the public must be cautious about accepting LLM transcriptions as definitive without careful review and comparison with the original source material.

Lack of Transparency: The inner workings of LLMs can be opaque, making it difficult to understand how the model arrived at a particular transcription. This lack of transparency can make it challenging to identify and correct biases or errors in the output.
To mitigate these risks, it's essential to:

Critically Evaluate LLM Output: Always cross-reference LLM transcriptions with the original documents and consult with experts when necessary.
Develop Robust Error Correction Methods: Invest in research and development of techniques for identifying and correcting errors in LLM-generated transcriptions.
Promote Transparency and Explainability: Encourage the development of LLMs that provide insights into their decision-making processes, making it easier to identify and address biases.

What are the ethical implications of using AI to potentially rewrite or reinterpret history through the transcription and analysis of historical documents?

The use of AI, particularly LLMs, in transcribing and analyzing historical documents raises significant ethical concerns regarding the potential to rewrite or reinterpret history:

Historical Revisionism: Inaccurate or biased transcriptions could inadvertently support historically inaccurate narratives or reinforce existing prejudices. This is particularly concerning when dealing with sensitive historical periods or marginalized communities whose voices have been historically silenced or misrepresented.

Erosion of Trust: If the public loses trust in the authenticity and reliability of historical sources due to concerns about AI manipulation, it could undermine the credibility of historical scholarship and erode faith in institutions responsible for preserving and interpreting the past.

Loss of Contextual Nuance: LLMs, while powerful, lack the nuanced understanding of historical context, language evolution, and cultural sensitivities that human historians possess. Over-reliance on AI could lead to interpretations that are technically accurate but miss subtle meanings or cultural nuances present in the original text.

Control Over Historical Narratives: The development and deployment of AI for historical research raise questions about who controls these technologies and whose interests they serve. There's a risk that powerful institutions or governments could use AI to promote specific historical narratives or silence dissenting voices.
To address these ethical challenges, it's crucial to:

Prioritize Human Oversight: Ensure that human historians play a central role in overseeing the use of AI in historical research, validating outputs, and providing contextual interpretation.
Develop Ethical Guidelines: Establish clear ethical guidelines for the development and use of AI in historical research, addressing issues of bias, transparency, and accountability.
Promote Critical Data Literacy: Educate the public about the potential benefits and limitations of AI in historical research, fostering critical thinking about the information they encounter.
Encourage Interdisciplinary Collaboration: Foster collaboration between historians, computer scientists, ethicists, and archivists to ensure that AI is used responsibly and ethically in the study of the past.
By carefully considering these ethical implications and implementing appropriate safeguards, we can harness the power of AI to enhance our understanding of history while preserving the integrity of the historical record.