Charset and Language Detection via Machine Learning Models
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Solution Overview
Problem
Existing technologies face challenges in accurately detecting the charset and language of received documents, especially when encoding scheme information is missing, leading to improper display and analysis of web content and emails, and require a mechanism for automatic detection to facilitate proper decoding and content analysis.
Innovation Solution
A computer-implemented method using language-based machine learning models trained on Unicode-style text documents in multiple languages to automatically detect the charset and language of a received document by converting the document into Unicode style and comparing vector representations against the models to determine the most similar charset and language combination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automatic charset detection is implemented to properly display web content, then display accuracy is improved, but system complexity increases due to multiple encoding scheme comparisons
Solution Approach 1:
The patent segments the charset detection process into distinct phases: collecting encoding information from multiple sources (HTTP headers, meta tags, BOM markers), evaluating the reliability of each source, and combining results systematically. This segmentation makes the complex detection process more manageable and accurate.
Solution Approach 2:
The patent performs preliminary actions by pre-establishing a hierarchy of encoding information sources and their reliability weights before actual detection occurs. Common encoding schemes are pre-identified and prioritized, allowing the system to quickly determine charset without exhaustive analysis of all possible encodings.
2Adaptability or versatility
If language detection is added to charset detection to enable content analysis, then content filtering capability is improved, but processing time increases
Solution Approach 1:
The patent merges charset detection and language detection into a unified process. By detecting both characteristics simultaneously rather than sequentially, the system achieves versatile content analysis capability while minimizing additional processing time. The combined approach leverages shared computational resources and data structures.
Solution Approach 2:
The detection system performs self-service by automatically adapting to different language and encoding combinations without requiring manual configuration. The system self-adjusts its detection parameters based on the detected charset and language pair, enabling versatile content analysis across multiple languages efficiently.
3Adaptability or versatility
If multiple encoding schemes are supported to handle global information exchange, then system versatility is improved, but difficulty in detecting correct encoding increases
Solution Approach 1:
The patent implements feedback mechanisms where the system evaluates the success of detected encodings by attempting to decode content and assessing readability. If decoding fails or produces garbage characters, the system feedbacks to try alternative encodings from the supported list, progressively refining the detection accuracy across multiple encoding schemes.
Solution Approach 2:
The patent creates equipotentiality by establishing a standardized evaluation framework for comparing different encoding schemes. Each encoding is assessed using consistent criteria (presence of BOM, HTTP header information, meta tag data), making the detection process systematic rather than chaotic despite supporting multiple schemes.
4Reliability
If charset information is collected from multiple sources (HTTP headers, meta tags, BOM), then detection reliability is improved, but processing complexity increases
Solution Approach 1:
The patent applies asymmetry by treating different information sources with different weights based on their reliability. HTTP headers with explicit charset parameters are given higher priority than meta tags, which in turn are prioritized over BOM markers. This asymmetric weighting simplifies the processing by establishing a clear hierarchy rather than treating all sources equally.
Solution Approach 2:
The patent applies local quality by optimizing the detection process for different contexts. When strong encoding information is present in HTTP headers, the system locally adapts by accepting that information without further verification. When headers are absent, it locally activates alternative detection methods using meta tags and BOM markers, adjusting the processing complexity to match the available information quality.
Data Source
AI summary
Language-based machine learning approach for automatically detecting universal charset and the language of a received document is disclosed. The language-based machine learning approach employs a plurality of text document samples in different languages, after converting them to a selected Unicode style (if their original encoding schemes are not the selected Unicode), to generate a plurality of language-based machine learning models during the training stage. During the application stage, vector representations of the received document for different combinations of charsets and their respective applicable languages are tested against the plurality of machine learning models to ascertain the charset and language combination that is most similar to its associated machine learning model, thereby identifying the charset and language of the received document.