Automatic Charset Detection Using SVM with Grouping
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Solution Overview
Problem
Existing technologies face challenges in accurately detecting the encoding scheme of received data, especially when encoding information is missing, leading to improper display and analysis of web content and ineffective spam and content filtering in emails.
Innovation Solution
A computer-implemented method using machine learning models, specifically Support Vector Machines (SVM), is employed to detect the encoding scheme of a target document by training on text document samples and applying feature selection techniques such as cross-entropy and TF-IDF to generate feature vectors, which are then used to compute similarity indicia and determine the most appropriate encoding scheme.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If encoding information is explicitly provided, then the encoding scheme can be correctly identified, but this type of information is often missing from many http servers and web pages
Solution Approach 1:
The system performs self-service by automatically detecting encoding schemes without requiring external encoding information. The SVM-based detector analyzes document content itself to determine the encoding, eliminating dependence on missing metadata or user input.
Solution Approach 2:
The patent introduces an intermediary encoding detection mechanism that acts as a mediator between the received data and the display system. This intermediary automatically identifies the encoding scheme before data processing, bridging the gap caused by missing encoding information.
2Ease of operation
If automatic charset detection is implemented, then web pages can be displayed correctly without encoding information, but the precision of detection needs to be improved
Solution Approach 1:
The patent replaces traditional mechanical or rule-based encoding detection methods with a machine learning-based SVM system. This substitution enables automatic detection while significantly improving precision through learned patterns from training data.
Solution Approach 2:
The system changes parameters by using multiple feature vectors (character frequency, word frequency, n-gram statistics) instead of single-parameter detection. The SVM classifier combines these multiple parameters to achieve high-precision automatic detection.
3Object-affected harmful factors
If content filtering is performed on emails, then spam and malicious content can be blocked, but this requires accurate charset detection which is often unavailable
Solution Approach 1:
The patent applies preliminary action by detecting the encoding scheme before content filtering and analysis. This preliminary encoding identification ensures that subsequent spam filtering and content analysis operations can proceed accurately on properly decoded email content.
Data Source
AI summary
The invention relates, in an embodiment, to a computer-implemented method for automatic charset detection, which includes detecting an encoding scheme of a target document. The method includes training, using a plurality of text document samples, to obtain a set of machine learning models. Training includes using a SVM (Support Vector Machine) technique to generate the set of machine learning models from feature vectors obtained from the plurality of text document samples. The method also includes applying the set of machine learning models against a set of target document feature vectors converted from the target document to detect the encoding scheme.