Background

A traditional manufacturer operating for over 30 years still relied heavily on paper-based document processing in its business workflows. From quality inspection reports, shipping/receiving slips, and meeting minutes to client contracts, factories generated paper records daily. As operations expanded, paper-based bottlenecks became critical, and with international clients requiring higher supply chain digitalization, digital transformation became imperative.

The company had previously attempted basic scan-and-archive solutions, but scanned images remained unsearchable, offering limited practical value. Furthermore, factory floor meetings and production instructions were largely communicated verbally, with no comprehensive written record — resulting in information gaps and difficulty tracking decisions.

Challenges Faced

• Tens of thousands of accumulated historical paper documents required digitization, including handwritten forms, printed records, and stamped invoices
• Traditional OCR tools have insufficient recognition accuracy for Chinese handwriting and complex tables
• Factory meetings and production instructions are communicated verbally, lacking a complete written record system
• On-site personnel communicate using a mix of Taiwanese and Mandarin, making speech recognition highly challenging
• Digitized data lacks intelligent management tools, preventing it from delivering its full value
• Varying levels of digital literacy among on-site personnel require an extremely simple and intuitive system interface

Industry Solutions

The company adopted LargitData's comprehensive digital transformation solution, integrating OCR (Optical Character Recognition), ASR (Automatic Speech Recognition), and AI-powered analytics to achieve a complete transition from paper-based operations to intelligent, data-driven workflows.

OCR Document Digitization

• Traditional Chinese OCR: Supports recognition of both printed and handwritten Traditional Chinese; actual recognition quality varies with scan resolution, handwriting neatness, stamp and handwriting overlap, and layout complexity — we recommend running a sample test with your company's actual documents before deployment
• Complex Table Recognition: Automatically recognizes table structures and converts table content into structured data
• Batch Processing Capability: Supports automated batch scanning and recognition processing for large volumes of documents

OCR Optical Character Recognition →

ASR Speech-to-Text

• Multilingual Speech Recognition: Supports real-time recognition and transcription of mixed Mandarin and Taiwanese speech
• Meeting Records: Automatically converts meeting recordings into verbatim transcripts with summarization and key point extraction
• Production Instruction Records: on-site verbal commands are transcribed in real time into written records, ensuring full traceability

ASR Speech-to-Text →

AI-Powered Analysis and Knowledge Management

• Automated Document Review: AI automatically identifies document types and archives them to the appropriate directories
• Full-Text Search System: all digitized documents are searchable by full-text keyword queries
• Intelligent data analysis: structured data extracted from documents is consolidated into visual management dashboards

Implementation Results

• The Chinese recognition accuracy for this project was 98%, with the test scope covering the handwritten forms and ruled-line documents the company actually uses
• Document processing efficiency from paper to digital increased by 75%, shifting manual data entry to batch scanning with human review
• Meeting minutes 100% digitized, with departmental meetings and shop-floor instructions preserved via speech-to-text, significantly improving communication traceability
• Paper printing and filing needs decreased, easing space pressure on physical filing cabinets
• Digitized data paired with full-text search lets employees locate document content directly by keyword, without having to dig through paper files
• Supply chain documents now have a searchable digital version, shortening preparation time when responding to customer audits and traceability requests

How to read these numbers: measurement basis and preconditions

The 98%, 75%, and 100% figures above are measurement results from this project, for specific document types over a specific period, and should not be treated as universal specifications for every scenario. Take recognition accuracy as an example: the same model can perform very differently on clean printed forms versus messy handwritten fields, and whether accuracy is calculated per character, per field, or per whole document changes the conclusion entirely.

When evaluating OCR and ASR solutions, we recommend confirming four things with the vendor: first, the unit used to calculate accuracy and the composition of the test set — ideally, ask for a re-run using your company's actual documents; second, which steps the efficiency-gain denominator includes, and whether time for manual review and exception handling has already been subtracted; third, how speech recognition performs with mixed Mandarin-Taiwanese usage, on-site noise, and multiple simultaneous speakers, and whether a custom terminology glossary is needed; and fourth, the process for handling recognition errors, including confidence-score thresholds, which fields require manual confirmation, and the traceability mechanism when an error occurs. Keeping a manual review ratio in the early rollout and adjusting it gradually based on actual results is usually more practical than going fully automated from day one.

A phased path from scanning to usable data

Digitization should not begin by scanning every archive. Select representative document and audio groups, then define the downstream use: full-text search, form extraction, compliance retention, meeting minutes or knowledge retrieval. Quality criteria differ by purpose. A searchable archive may tolerate minor layout loss, while field extraction requires accurate tables, dates, amounts and identifiers. Samples should include poor scans, handwriting, stamps, mixed languages, background noise and overlapping speech.

The production design needs checkpoints for source intake, preprocessing, recognition, confidence thresholds, human correction, export and retention. Low-confidence fields should enter a review queue instead of silently reaching a business system. Keep the original file, extracted result, engine version and correction history together so errors can be traced and reprocessed when models improve.

• Prioritize collections by business value, risk and expected reuse.
• Set separate acceptance metrics for text, layout, tables and key fields.
• Use difficult real-world samples rather than clean demonstration files.
• Route low-confidence output to review and record every correction.
• Preserve source, model version and lineage for audit and reprocessing.