Dynamic OCR Processing Configuration
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Solution Overview
Problem
Existing optical character recognition (OCR) systems lack flexibility in determining whether to perform processing locally on a device or remotely, often relying on fixed criteria and not optimizing based on real-time conditions such as device capabilities and network connectivity.
Innovation Solution
A dynamic configuration system that determines whether to perform OCR processing locally or remotely based on device type, image type, available memory, and network connectivity, allowing for flexible decision-making and efficient resource allocation between mobile devices and OCR servers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If OCR processing is always performed remotely on a server, then processing power and computational resources are centralized, but network dependency increases and processing speed may be reduced due to network latency
Solution Approach 1:
The system dynamically determines the OCR processing location based on real-time conditions such as network connectivity status, device capabilities, and image characteristics. The decision is not fixed but adapts to current system state, allowing the architecture to switch between cloud and local processing modes optimally
Solution Approach 2:
The mobile device acts as an intermediary that can either process images locally or transfer them to the server. This intermediary role allows the system to flexibly route processing tasks based on conditions, eliminating the need for direct server processing for all cases and reducing network dependency when conditions permit local processing
2Productivity
If OCR processing is performed locally on all devices, then processing speed improves and network dependency decreases, but legacy devices without sufficient computational resources cannot perform advanced OCR processing
Solution Approach 1:
The system evaluates device parameters such as processing power, memory availability, and OCR engine capabilities to determine whether local processing is feasible. By dynamically assessing these parameters, the system adapts to both legacy and modern devices, routing appropriate tasks to the server while enabling local processing when device capabilities permit
Solution Approach 2:
The OCR processing capability is segmented between device and server based on capabilities. Modern devices with sufficient resources handle their own processing, while legacy devices offload to the server. This segmentation allows each device type to operate within its capability envelope, maintaining overall system versatility
3Device complexity
If a fixed architecture is used for OCR processing, then system complexity is reduced, but flexibility to adapt to different device capabilities and real-time conditions is lost
Solution Approach 1:
The architecture transitions from a fixed to a dynamic model where the processing location (device or server) is determined at runtime based on current conditions. This dynamic determination enables the system to adapt to varying device capabilities, network states, and image characteristics without requiring a completely complex reconfigurable architecture
Solution Approach 2:
The system incorporates feedback mechanisms that evaluate device type, available memory, network connectivity, and image properties to make informed decisions about processing location. This feedback-driven approach provides flexibility while maintaining manageable system complexity through rule-based decision logic
Data Source
AI summary
Dynamically configuring OCR processing may include determining a device type and determining whether to perform optical character recognition (OCR) processing of the received image locally based on one or more OCR parameters. Example OCR parameters may include the device type, the image type, the size of the received image, the available amount of the memory, the measured/benchmarked throughput of OCR processing on the device relative to an OCR server throughput and network throughput, and/or the current level of network connectivity. If it is determined that OCR processing of the received image should be performed locally, the device may compute one or more name-value pairs corresponding to the received image and transmit the name-value pairs to a remote data server for processing.