Checklist Generation and Dimension Extraction for a Major Steel Corporation

A leading steel manufacturer sought to automate the extraction of dimensions, tolerances, and symbols from CAD drawings, along with the generation of checklists used across manufacturing operations. iCaptur implemented an OCR- and machine learning–based workflow to streamline these processes across non-standard drawing formats.

The Challenge

The client needed to extract dimensions, tolerances, symbols, and generate dimensional checklists from a wide range of CAD drawings used across manufacturing operations. These drawings varied widely in format and layout, making standardization difficult. As a result, engineers had to manually review each document to identify essentials.

This manual process was time-intensive, prone to inconsistencies, and limited how effectively drawing data could be reused within engineering, quality, and production systems. The lack of structured data slowed downstream workflows and increased operational friction.

Solution

iCaptur implemented an AI-driven document understanding workflow using structured pipeline: OCR → ML classification → extraction →checklist → dashboards → integrations.

OCR accurately interpreted technical text, symbols, and dimensional annotations commonly used in steel manufacturing drawings. Machine learning was used to classify drawings by type and automatically identify relevant dimensions, tolerances, and symbol sections. Key data points including dimensions, tolerances, and checklist parameters were extracted and converted into standardized, structured formats. Based on this extracted information, dimensional checklists were generated automatically.

The extracted information was made available through searchable dashboards and securely delivered via APIs, enabling seamless integration with the client’s existing engineering and manufacturing systems.

Outcome

The solution delivered consistent extraction of dimensions, tolerances, and symbols, along with reliable generation of dimensional checklists across non-standard drawings, while reducing manual interpretation effort and related errors.

Extracted information became searchable, classifiable, and reusable across teams. By transforming unstructured drawings into structured, system-ready data, the client improved efficiency across engineering and production workflows and strengthened the overall use of engineering intelligence across the organization.