Examples of packaging coding and label identification application solutions

Program overview

Examples of application solutions for packaging inkjet coding and label recognition are examples of public industry application solutions in the direction of food and medicine vision solutions. For food, medicine, and daily chemical packaging production lines, it presents visual inspection solutions for inkjet characters, label positions, barcode QR codes, sealing status, and packaging integrity. This page is geared towards project evaluation and solution matching. It does not disclose customer names, production line scales, business data or unconfirmed operating indicators. It focuses on what similar computer vision projects need to be evaluated, how to organize the technical route, how to confirm the delivery content, and what information should be prepared for early communication.

Project background

Packaging coding and label identification are suitable for food, medicine, daily chemicals, electronics and logistics packaging links. The project not only identifies the character content, but also checks the position, completeness, clarity, label orientation and barcode QR code readability and correlates it with the traceability system. In project communication, it is first necessary to clarify the detection objects, work station locations, sample differences, rhythm requirements, installation space, existing equipment interfaces and acceptance methods. For scenarios such as outer carton coding, bottle labeling, medicine box packaging, logistics manifests, etc., the same visual concept may require different camera angles, light source methods, lens focal lengths, trigger methods, and judgment rules at different sites. Therefore, JIVISION usually starts with sample analysis and imaging verification, rather than directly applying a fixed template.

Customer needs and assessment points

Typical requirements for this type of project include inkjet printing, label integrity, barcode QR codes, and traceability associations. In addition to the identification itself, it is also necessary to evaluate whether the test results need to retain pictures, whether the formula needs to be switched according to product model, whether it needs to be connected with PLC, robot, MES, WMS or host computer system, and whether batch reports need to be generated. The main pain points include: the printing characters are affected by the material, surface, speed and ink color, and are prone to hyphenation, ghosting or missing printing; the label position, direction and barcode readability directly affect factory compliance and traceability; the packaging site is fast and needs to stably handle different specifications and different batches of products. These issues need to be confirmed through sample testing and field condition analysis, and are not suitable to be replaced by unverified universal indicators.

Technical solution

The technical solution revolves around inkjet printing, label detection, and packaging integrity, and is implemented in conjunction with the project delivery process. JIVISION will first evaluate the imaging stability, and then design the algorithm link and system interface. Solutions usually include: improving the imaging quality of high-speed production lines through trigger acquisition, stroboscopic light sources and appropriate viewing angles; combining OCR, barcode recognition, label area detection and sealing/packaging status judgment; supporting formula switching, abnormal rejection signals, batch records and report exports. In actual projects, traditional image processing, deep learning detection, OCR recognition, instance segmentation, point cloud processing or rule review processes can be combined according to object characteristics, so that the system can both identify targets and explain the basis for judgment.

System architecture

• character imaging
• OCR recognition model
• barcode decoding
• Traceability interface

Implementation process

The implementation process generally includes requirement confirmation, sample collection, imaging experiments, solution verification, algorithm training or rule development, interface definition, on-site deployment, acceptance testing, and operation and maintenance handover. At each stage, sample conditions, parameter versions, judgment rules, and abnormal sample records will be precipitated to facilitate subsequent review. When customers subsequently add product models, defect categories or on-site workstations, they can continue to iterate based on the original data and interfaces.

Delivery content

• Coding recognition module
• Label position detection
• Exception elimination interface
• Batch report

Acceptance and iteration

The acceptance indicators should be determined based on customer samples, on-site test results and testing standards confirmed by both parties. Common evaluation dimensions include recognition accuracy, risk of missed detection, false alarm handling, processing speed, stability under illumination changes, data traceability and maintenance convenience. The solution example description on the public page is used to illustrate the technical route and delivery method. The final project indicators should be based on the customer's real samples, deployment environment and contract agreements.

Applicable scenarios

This solution example is suitable for outer box coding, bottle labeling, pill box packaging, logistics manifests and similar needs. It is also suitable for reference by customers who are evaluating machine vision inspection, computer vision algorithms, edge AI vision, 3D vision measurement, robot vision or visual data training. For projects with uncertain technical routes, sample evaluation, imaging testing, and small-scale solution verification can be conducted first to confirm feasibility before proceeding to system development and on-site deployment.

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