Proactive Machine Translation via Image Quality Gatekeeping
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Solution Overview
Problem
Existing machine translation tools on mobile devices often fail to provide accurate results due to the difficulty in capturing high-quality images, especially under varying conditions, which limits the effectiveness of conventional optical character recognition (OCR) techniques.
Innovation Solution
A computer-implemented method that processes translation requests by evaluating image data quality and generating or retrieving translation information based on metadata, allowing for accurate translation even with low-quality images, and enabling the use of substitute or alternate inputs to avoid processing unsuitable image data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional OCR techniques are used to extract text from image data, then translation accuracy depends on image quality, but mobile devices often capture images of insufficient quality under varying conditions
Solution Approach 1:
The patent introduces an intermediary evaluation mechanism that assesses image quality metrics (resolution, lighting, focus, text clarity) before proceeding with OCR. This intermediary step acts as a gatekeeper, determining whether the image data is suitable for conventional extraction or if alternative approaches should be used, thereby protecting the overall translation reliability from poor image quality
Solution Approach 2:
The system dynamically changes processing parameters based on image quality assessment. When image quality is insufficient, the system switches from conventional OCR extraction to alternative methods such as metadata-based translation or hybrid approaches, effectively adapting the text extraction strategy to the actual image conditions to maintain translation accuracy
2Productivity
If the system processes all translation requests through conventional OCR extraction, then consistent processing workflow is maintained, but computational resources are wasted on unsuitable image data
Solution Approach 1:
The patent implements preliminary image quality evaluation and suitability assessment before initiating full OCR processing. By pre-evaluating key parameters such as resolution, lighting conditions, and text visibility, the system identifies unsuitable images early and routes them to alternative processing paths, preventing wasteful consumption of computational resources on images that cannot be successfully processed
Solution Approach 2:
The translation processing workflow is segmented into distinct stages: image quality evaluation, suitability determination, and conditional processing path selection. This segmentation allows the system to apply different processing strategies to different types of input images, optimizing resource allocation by applying heavy OCR processing only to suitable images while using lighter alternative methods for unsuitable ones
3Adaptability or versatility
If the system attempts translation with low-quality images, then service availability is maintained, but translation accuracy deteriorates
Solution Approach 1:
The system dynamically adjusts its processing strategy based on real-time image quality assessment. Rather than using a fixed workflow, the system adapts its approach by selecting different text extraction and translation methods according to the specific characteristics of each input image, maintaining service availability while optimizing translation accuracy for each case
Solution Approach 2:
An intermediary quality assessment mechanism mediates between the input image and the translation processing pipeline. This intermediary evaluates image suitability and determines the appropriate processing path, ensuring that low-quality images are handled through alternative methods that maintain service availability while preserving translation accuracy as much as possible
Data Source
AI summary
In one embodiment, a computer-implemented method for proactively improving machine translation in real time by processing a translation request includes: receiving the translation request from a mobile device; and either generating or retrieving translation information based on a result of either or both of: determining whether the translation request includes image data suitable for generating a machine translation; and determining whether the translation request includes metadata suitable for generating an estimated translation. In another embodiment, a computer-implemented method for proactively improving machine translation in real time by generating and submitting a translation request includes: capturing image data; evaluating one or more conditions corresponding to the captured image data; generating metadata corresponding to the captured image data; and generating and submitting the translation request to a machine translation platform. Corresponding systems and computer program products are also disclosed.