AI-assisted practical guide. Examples are hypothetical; these are proposed editorial methods, not reported research results.
Optical Character Recognition often struggles with archaic fonts or damaged paper, resulting in transcription errors that can distort the meaning of a historical record. When you possess a short original sample, such as a high-resolution image or a manual transcription, you can use a large language model to identify where the OCR has failed. The goal is to isolate discrepancies in meaning-bearing characters while keeping uncertain spans intact for further human review.
Prompting for Meaningful Corrections
To begin, provide the AI with both the original text and the OCR output. Instruct the model to compare the two strings and identify substitutions, omissions, or additions. Suggest that the AI ignore minor formatting differences but flag any change that alters the semantic value of a word. You should ask the model to output the corrected text while wrapping any segments it cannot confidently resolve in brackets. This is intended to help check that the AI does not hallucinate a correction where the original source is illegible.
Hypothetical example
Imagine you have an original handwritten note that reads "The shipment arrived on Tuesday" but the OCR output is "The sh1pment arrlved on Tuesday". You might use a prompt such as: Compare the original text to the OCR text. Correct meaning-bearing characters and mark uncertain spans with brackets. Original: The shipment arrived on Tuesday. OCR: The sh1pment arrlved on Tuesday. The AI output would be: The shipment arrived on Tuesday. If the original was smudged and the OCR read "The sh1pment arrlved on [unclear]", the AI output should be: The shipment arrived on [unclear].
Verifying the Final Transcription
Once the AI generates the corrected version, you must perform a manual audit to ensure the model did not over-correct. Compare the AI output directly against the original source image or text, specifically looking at the bracketed spans. Check that the AI did not replace a legitimate, rare word with a more common one simply because it fit a modern linguistic pattern. The final deliverable is successful if every character in the corrected text corresponds to a visible mark in the original, and every illegible mark remains flagged as uncertain.