How can enterprises in the high-end manufacturing industry implement AI OCR recognition software?

Introduce recognition capabilities covering all invoice types, automatically extract and structurally output invoice information, directly write it back to business systems, and eliminate manual entry one by one; automatically connect to tax authority verification to check invoice numbers, amounts, invoicing dates, and issuer status, and perform cross-period duplicate reimbursement comparison; separately push entries with low recognition confidence for manual review, with the rest of the process fully unattended.

What problems existed before Dongguan Yinzhou Environmental Packaging launched AI OCR?

Procurement in the manufacturing stage is spread across raw materials, parts, molds and tooling, equipment spare parts, outsourced processing, and logistics. Suppliers include both large manufacturers with long-term agreements and small and micro suppliers providing temporary replenishment, with varying invoice types and arrival rhythms; invoice authenticity verification requires logging into the tax bureau end to query each one, and manual work can only spot-check, not cover the full volume; entry errors propagate along certification, deduction, and vouchers, making post-correction costly.

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High-end manufacturingAI-OCR

Dongguan Yinzhou Environmental Packaging · Environmental packaging manufacturer

Dongguan Yinzhou Environmental Packaging | Automatic document OCR recognition

Recognition, authenticity verification, and duplicate checkingCore Capabilities

Replace manual entryCore Value

Enterprise overview

Dongguan Yinzhou Environmental Packaging Co., Ltd. was jointly founded in 2020 by Dongguan Jinzhou Paper and Dongguan Zhongtang Huangyong Economic Development General Company. Covering about 70 mu, it is an influential corrugated packaging enterprise in South China, leading local peers in investment scale and production capacity. The company mainly produces and sells corrugated cardboard, cartons, and color paper boxes. Phase I built one 2.8-meter and one 2.5-meter imported BHS production line each; Phase II added BHS and Fosber high-speed cardboard production lines, specializing in three-layer, five-layer, seven-layer, and continuous cardboard. After full completion, the project will have an annual capacity of 850 million square meters of cardboard and 220 million cartons, and together with Jinzhou Paper will build a full industry chain model of papermaking-cardboard-cartons.

Business pain points

Procurement in the manufacturing stage is spread across raw materials, parts, molds and tooling, equipment spare parts, outsourced processing, and logistics. Suppliers include both large manufacturers with long-term agreements and small and micro suppliers providing temporary replenishment, with varying invoice types and arrival rhythms. Invoice authenticity verification requires logging into the tax bureau end to query each one, and manual work can only spot-check, not cover the full volume. Entry errors propagate along certification, deduction, and vouchers, making post-correction costly.
Duplicate reimbursement relies on memory and ledger comparison, and duplicates across periods and departments are hard to detect. Under spot-check methods, abnormal invoices are often exposed only at the certification stage, by which time the business has already completed payment
Voided invoices and red-flushed invoices mixed into accounts directly affect input VAT deduction and cost recognition. After China Golden Tax Phase IV, tax comparison tends toward full quantification. Once invoice authenticity has problems, it directly affects deduction, and spot-check verification can no longer cover regulatory requirements

Solutions

Introduce recognition capabilities covering all invoice types, automatically extract and structurally output invoice information, directly write it back to business systems, and eliminate manual entry one by one
Automatically connect to tax authority verification to check invoice number, amount, invoicing date and issuer status, and perform cross-period duplicate reimbursement comparison
Items with low recognition confidence are separately pushed for manual review, while the rest of the full process is unattended
Ultimately upgrades verification coverage from sampling to full volume, intercepting anomalies before booking, with verification records retained for future reference
And opening recognition and verification capabilities to other business systems for invocation, avoiding duplicate construction of similar capabilities across systems