Room specifications need no manual transcription
This example shows the module’s role in a hotel process. We define the source scope and available actions for each implementation.
Image-based PDF
The sales department receives an up-to-date equipment specification as a scan. Searching the file does not work, and the table contains several room layout options.
Readable content and flagged uncertainties
OCR recreates the table, links the data to the file and highlights sections that need checking. Only an approved version can inform offer knowledge; an illegible footnote is not turned into certain information.
- Searchable text
- Table in a structured format
- Link to the scanned page
What does this module cover?
Reading can cover rules, instructions, price lists and selected forms. We agree the fields and required level of accuracy for each document type, rather than promising identical results for every file.
Content recognition
Text stored in an image becomes available to search and subsequent tools.
Preserving structure
Headings, columns and tables help prevent values from different offer variants being confused.
Verification queue
Uncertain fields are returned to a staff member with the original document extract.
How do we launch the solution?
Selecting documents
We collect samples, check quality and agree the required fields and exclusions.
Reading and structuring
We recognise the content and layout and link the result to the source file.
Approval
A staff member checks uncertain sections before the result is used in a process.
What goes into the module and what comes out?
We separate source material from the tool’s output. This helps subsequent modules understand which context they can use.
What does AI use?
- Scanned PDFs and document images
- Field or table definitions
- Result approval rules
What does the team receive?
- Text from the document
- Structured fields and tables
- List of sections to verify
Permissions and decisions remain with the hotel
Before launch, we agree data access, responsible people and how exceptions will be handled.
The hotel decides which documents are added to the shared knowledge base. Reading a file does not by itself mean that it has been approved or shared with every staff member. Documents containing personal data require restricted scope and access.
OCR does not assess whether a document is accurate or up to date. It does not provide a binding interpretation of contracts or make payments. A poor-quality photo may need to be scanned again.
What changes in day-to-day work?
Document data can be used without manually transferring every field.
A staff member can check which document an answer comes from.
Which sources and tools do we connect the module to?
OCR is a data preparation stage. Searching, interpreting and using the result in an offer belong to separate, subsequently defined modules.
Related process elements
Its place in the implementation journey
These links come from the roadmap. Requirements form the basis of each node, while enrichments extend its capabilities. This does not mean that all subsequent modules must be launched at once.
Modules this element enriches
Frequently asked questions
Does every PDF need OCR?
No. If a file contains a valid text layer, reading it is often sufficient. OCR is primarily useful for scans and photographs.
What about handwriting?
We assess the feasibility and quality of recognition using samples. We do not assume that every handwritten form can be processed reliably.