Finding a word does not prove an identifier was read correctly
You have scanned a file and the PDF supports search. Before delivery, another question remains: does the recognised text preserve the identifier, the negative instruction and the source page? For a document digitisation service, this guide proposes a small trial: use a text assistant to prepare a list of differences between a human reference and OCR output, without letting it rewrite the document.
The intended output is a review queue: page, field, both readings and an outstanding decision. We include a fictional example with an O, a zero and a missing negative. We wrote the comparison ourselves; it is not output from an executed trial and does not demonstrate AI accuracy.
Image, searchable text and index: define what you are checking
Adobe explains that Acrobat can create a searchable text layer through OCR and recommends reviewing its accuracy and completeness afterwards. Its correction documentation lets users compare the image with recognised text when it flags uncertain words. This is manufacturer documentation; we have not tested the application or its interface here.
Providers also separate tasks. Active describes OCR and metadata extraction; Punto 21 presents OCR, ordering and indexing, and quality control in separate sections. Their pages show how they describe their services, rather than results verified by Adaris. For this pilot, separate three deliverables: the received image, the recognised text and the agreed field index.
Do not consider the index checked because the PDF can find a word. Do not use the assistant to certify completeness, accessibility or documentary validity either. If those requirements are part of the job, they need their own criteria and checks. Keep the original capture identified alongside working versions; this trial does not propose destroying paper or changing originals.
Fictional case: two pages, three differences and an unresolved reading
Everything below is invented. R1 is a reference transcription that a fictional operator has checked against the image; it also records an illegible area. O1 represents invented OCR output. No real document is attached and the assistant is not reading images. Preserve literal text, including hyphens, uppercase O and zero. The sample document remains in Spanish in both versions so that the character comparison is identical.
| Location | R1 · human reference and notes | O1 · fictional OCR text |
|---|---|---|
| Page 1 · box | Caja Q-08 | Caja Q-O8 |
| Page 1 · reference | Referencia AB-O7 | Referencia AB-07 |
| Page 1 · instruction | No incluir anexo | Incluir anexo |
| Page 2 · series | Operator note: first character illegible, possibly O or 0; followed by -24. | Serie O-24 |
The manual review to use as the trial reference
Before asking AI for help, prepare these comparison decisions. On page one, three discrepancies must go back to the operator: Q-08 versus Q-O8, AB-O7 versus AB-07 and the loss of “No”, changing “Do not include the appendix” to “Include the appendix”. Do not normalise every identifier to digits: the fictional reference contains an O in AB-O7.
On page two, these texts cannot resolve the first character. R1 records uncertainty while O1 contains an O; that output does not prove what was in the image. The row must remain pending source comparison. Do not choose zero because it seems more logical or mark the series as validated.
This reference lets you check whether the assistant preserves differences and limits alike. Even a correct table requires checking the image and page correspondence before authorising changes in a working copy. If the source remains illegible, record the issue and consult the agreed delivery criteria; do not fill in the character from context.
Trial instruction: compare, cite and keep uncertainty open
Use only the example’s fictional texts in an authorised assistant. Before introducing real documents, check permissions, processing terms, access and retention. Do not paste case files, signatures or personal information to try this exercise.
- Proposed prompt: “Compare R1 and O1 by page and field. Return both literal readings, the type of difference and what the operator must check. Preserve letters, digits, hyphens and negatives. R1 includes uncertainty notes: do not turn them into recognised text or a decision. Do not complete illegible characters, normalise identifiers or use outside knowledge to decide. Do not modify files, indexes or delivery states. Cite the input passage supporting each row. Flag missing locations or references.”
- Then compare the response with the manual reference above. The prompt requests behaviour; it does not guarantee model compliance. A proposal to replace AB-O7 with AB-07 or resolve the series on page two must be returned for correction.
- Try a variant: remove page one’s R1. The output can no longer declare its readings verified; it must flag the missing reference for that comparison. Another variant: add a human note confirming 0-24 on page two. It can now flag the difference from O1 while preserving who confirmed it; it still does not authorise an edit.
A first pilot that counts correction work too
Prepare a small set of fictional texts with known differences and others without differences. Keep a manual reference per page. Record missed discrepancies, invented corrections, lost locations and uncertainty turned into certainty. Measure the full time to prepare, review and correct the list; compare it with preparing the same list manually. We have no results and promise no savings.
Agree beforehand which errors prevent proceeding. For example, if the assistant invents a character or changes a negative instruction, stop that proposal and investigate its cause. A list with no discrepancies does not prove that every page is present, ordered or well captured: those checks remain part of the operator’s document quality control.
When a manual trial needs custom software
Start by copying the example and reviewing the output. Automating the process requires defining links between image, page, OCR text, reference and human decision, with permissions, history and recovery. Integration with your scanner, PDF editor or document management system must be checked case by case; this guide claims compatibility with none.
Adaris can assess custom software, CRM or AI automation for that specific process. A limited first integration could prepare issues for review and record their resolution, if the data and tools allow it. This is not an existing proprietary digitisation product or automatic document approval. Commercial support is in Spanish.
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Are OCR differences getting lost between files and emails?
Tell us how you link pages, references and decisions. We can assess a custom process; commercial support is in Spanish.