From Paper to Pixels: On-Device AI’s Game-Changing Impact on Logistics Information Extraction

The Economic Times reports that there are approximately four billion paper documents in circulation at any given time within the $25 trillion global cargo trade. And each one holds critical details that keep goods moving. A single delay in extracting this information can slow down deliveries, increase costs, and create inefficiencies across the supply chain. To avoid these disruptions, logistics companies need a solution that captures and processes key data instantly.

On-device AI is making this possible by running machine learning models directly on mobile devices, eliminating the need for cloud-based processing. This approach removes delays linked to cloud-based processing, ensuring immediate insights while securing sensitive data. Shipping labels, bills of lading, and warehouse forms can be scanned and processed in real-time, reducing manual input and accelerating workflows.

By minimizing reliance on cloud servers, on-device AI speeds up operations and strengthens data privacy.

As demand for speed and accuracy continues to rise, AI-driven document data extraction is becoming necessary for logistics businesses looking to stay ahead.

The Technical Imperative for On-Device AI

Traditional cloud-based solutions often struggle with latency and privacy concerns, which can be significant issues in logistics. A delay of just a few seconds in processing a shipping label or bill of lading can disrupt an entire supply chain, leading to shipment errors, missed deadlines, and increased operational costs. Additionally, cloud-based processing depends on internet connectivity, making it unreliable in warehouses, ports, and remote distribution centers where network coverage may be inconsistent.

On-device inference addresses these challenges by processing data locally. Instead of sending scanned documents to the cloud and waiting for a response, the AI extracts key data points instantly, reducing reliance on manual entry and improving workflow efficiency. This transformation enhances speed and also keeps sensitive logistics data on the device, reducing exposure to cybersecurity risks.

A study by IBM found that AI-driven automation in logistics can reduce document processing times by up to 80%. By eliminating cloud-related delays, on-device AI supports real-time decision-making, whether in last-mile delivery, inventory management, or warehouse operations.

PackageX’s multi-platform vision SDK, which is available for iOS, Android, React Native, and Flutter simplifies integration by handling model conversion, optimization, and deployment. Developers can incorporate on-device AI into their applications without managing platform-specific complexities, allowing businesses to improve logistics processes with minimal development effort.

Converting and Optimizing Complex Models

Processing AI models directly on mobile devices requires adapting research-grade models into formats that balance efficiency and accuracy. This process involves selecting the right frameworks, optimizing for hardware constraints, and applying techniques that reduce computational load without sacrificing precision.

Model Conversion

One major challenge in deploying on-device AI is converting various models into mobile-friendly formats without compromising performance. PackageX addresses this by converting state-of-the-art deep learning models for different modalities such as text, vision, and multimodal models into formats optimized for mobile platforms.

• Core ML (iOS): PackageX primarily utilizes CoreML for iOS - which makes it easier to  utilize Apple’s Neural Engine for highly optimized inference.
• LiteRT (Formerly TensorFlow Lite) (Cross-Platform):  More easily supports utilizing Android NPU-specific delegates for better performance.
• ONNX (Cross-Platform): Offers flexibility by supporting multiple runtimes, making it ideal for cross-platform applications.

Each format has specific advantages based on the target hardware. Core ML provides seamless integration with iOS, LiteRT optimizes performance on Android, and ONNX enhances compatibility across different systems. PackageX abstracts these conversion complexities, allowing businesses to focus on creating value via a simple integration experience across different platforms..

Importance of Fused Operations

Reducing computational overhead is critical for on-device AI. PackageX optimizes fused operations, where multiple layers are combined into a single execution step, reducing redundant calculations and improving inference speed. Frameworks like CoreML and LiteRT take advantage of hardware-specific acceleration to execute these operations efficiently.

Quantization and Optimization

Optimizing model size while maintaining accuracy is essential for real-time processing. We employ industry-leading quantization techniques:

• Post-Training Quantization (PTQ): Converts floating-point weights (e.g., FP32) into lower-precision formats like INT8, reducing memory usage and speeding up execution.
• Quantization-Aware Training (QAT): Applies quantization adjustments during training to maintain accuracy levels, ensuring reliable results in real-world applications.

For logistics operations,  PackageX chooses the optimal quantization levels that reduces inference time by 40 % while maintaining a 95 % accuracy in document text extraction with document automation. This directly improves workflow automation by minimizing delays in processing critical logistics documents.

Post-processing Pipeline:

We employ a strong validation, post-processing, and correction pipeline on top of our model predictions – to ensure that they make sense – and if we can extract any more information. If needed, we aggregate predictions from multiple models while keeping it near real-time.

By integrating these advanced techniques, PackageX delivers real-time AI-powered solutions that enhance efficiency across logistics and supply chain operations.

Bridging the Platform Divide

iOS offers a streamlined experience with Core ML and the Neural Engine, making AI deployment more straightforward. Android, however, presents a different scenario. Devices vary in processing power, and not all include a dedicated neural processing unit (NPU). Even when NPUs are available, they often require vendor-specific delegates like Qualcomm’s QNN, MediaTek’s Neuron, or Samsung’s ENN, which can complicate implementation.

Inference frameworks further add to these differences. LiteRT (formerly TensorFlow Lite) and ONNX each have unique optimization and conversion complexities. Not all network layers support conversion to every format. LiteRT is popular for its compatibility with TensorFlow models and efficient performance on mobile devices. ONNX, by contrast, offers broader model support, enabling developers to work across multiple deep-learning frameworks.

To address these challenges, our vision SDK abstracts platform-specific variations, providing a unified API that adapts automatically to the underlying hardware. Whether a logistics application runs on a high-end iPhone or an entry-level Android device, the SDK ensures that document extraction remains accurate. This consistency allows businesses to integrate AI-powered logistics solutions without worrying about device-specific adjustments, reducing development time and improving operational efficiency.

Delivering a Complete End-to-End Solution

Vision SDK empowers businesses and software developers by streamlining the integration process:

• Unified Multi-Platform Support:
  With built-in support for iOS, Android, React Native, and Flutter, our SDK makes it easy to deploy on-device AI solutions across diverse hardware environments. This minimizes the engineering effort needed to adapt models to different operating systems and hardware configurations.
• Seamless Integration:
  The SDK provides well-documented abstractions encapsulating model conversion, quantization, and inference optimization. This enables you to integrate real-time document extraction functionalities into your larger enterprise systems, whether it’s a warehouse management system (WMS), enterprise resource planning (ERP), or building management system (BMS).
• Operational Resilience and Privacy:
  By processing data locally, our solution enhances data security and ensures continuous operation even in low-connectivity scenarios. This is particularly important in logistics, where any downtime or delay can disrupt the entire supply chain.

Future Trends in On-Device AI for Logistics

With rapid advancements in mobile hardware, on-device AI is set to become even more powerful, driving greater efficiency in logistics operations. Several key trends are shaping the future of AI-driven document extraction:

• Neural Processing Units (NPUs): The integration of dedicated AI chips in mobile devices will significantly enhance real-time document processing, enabling faster and more accurate data extraction without cloud dependency.
• Federated Learning: AI models will evolve through decentralized updates across multiple devices, ensuring continuous improvements in accuracy while maintaining data privacy, which is an essential factor in logistics security and compliance.
• Augmented AI: A hybrid approach combining on-device and cloud-based AI will optimize performance, balancing real-time processing with the ability to leverage cloud resources when needed.

As these innovations unfold, solutions like PackageX’s vision SDK will continue to play a pivotal role in streamlining AI deployment across diverse hardware ecosystems, enabling logistics companies to stay ahead with AI driven automation.

How PackageX Can Help?

On-device AI is transforming logistics operations by enabling real-time information extraction from critical documents. Through advanced model conversion, smart quantization, and optimization, PackageX delivers rapid and reliable insights while addressing the challenges of diverse mobile platforms.

The multi-platform PackageX vision SDK simplifies model deployment across iOS, Android, React Native, and Flutter, allowing developers to integrate AI-driven solutions seamlessly. Whether implementing a complete system or enhancing enterprise applications, on-device AI ensures the speed, privacy, and efficiency required to stay competitive in logistics.