Cloud-Based MFP Translation Service with Context-Specific Rule Correction
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Solution Overview
Problem
Existing multi-function peripherals (MFPs) face challenges with inaccurate optical character recognition (OCR) and natural language translation due to lack of context-specific processing, leading to unsatisfactory results for users.
Innovation Solution
A cloud-based system that uses context-specific rules sets associated with unique MFP identifiers to enhance OCR and translation accuracy, allowing user corrections to update these rules for improved future processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automatic OCR processing is performed by the MFP, then processing speed is improved, but accuracy deteriorates due to lack of context-specific rules
Solution Approach 1:
The patent introduces a cloud-based server as an intermediary between the MFP and the final OCR output. The server receives scan data from the MFP, applies context-specific rules from selected rule sets, and returns corrected text. This intermediary layer maintains the speed benefit of automatic processing while improving accuracy through specialized correction rules tailored to different document types and languages.
Solution Approach 2:
The system changes the parameter of processing context by selecting different rule sets based on document characteristics. Instead of using a single fixed OCR processing mode, the system dynamically adjusts processing parameters by choosing appropriate context-specific rule sets that match the document type, language, or other identifying features, thereby improving accuracy without sacrificing processing efficiency.
2Measurement precision
If context-specific rules are applied to improve accuracy, then OCR and translation accuracy are improved, but system complexity increases due to multiple rule sets
Solution Approach 1:
The patent creates a universal rule set architecture where a single cloud-based server handles multiple document types, languages, and contexts through a collection of reusable rule sets. Instead of embedding complex context-specific processing in each MFP, the system uses a universal platform that selects and applies appropriate rules based on document identification, reducing individual device complexity while maintaining high accuracy across diverse scenarios.
Solution Approach 2:
The system performs preliminary classification of documents to identify their context (language type, document format, etc.) before applying specific correction rules. This preliminary action of categorization allows the system to efficiently select the appropriate rule set without requiring complex real-time analysis during the actual OCR processing, thereby managing system complexity while maintaining high accuracy.
3Reliability
If user corrections are used to update rules, then continuous improvement is achieved, but data processing complexity increases
Solution Approach 1:
The patent implements a feedback mechanism where user corrections to OCR or translation output are captured and used to update the context-specific rule sets in the cloud. This feedback loop continuously improves processing reliability by learning from actual user corrections. The complexity is managed by automating the rule update process and storing corrections in a structured format that can be systematically processed to generate improved rules.
Data Source
AI summary
Techniques are provided for translating a document that was scanned by a multi-function peripheral (MFP). A server within a computing cloud receives an MFP identifier and processed scan data that results from optical character recognition and/or natural language translation having been performed on scan data produced by the MFP. In response to the receipt of the processed scan data at the server, the server selects a set of rules that is mapped to a context to which the MFP identifier is mapped. Corrected processed scan data is generated by applying the set of rules to the processed scan data that was received by the server. Manual corrections made to the corrected processed scan data may be used to update the set of rules so that those corrections are also made to other processed scan data produced by MFPs having identifiers mapped to the same context.