Garbled Text Parsing via Segmentation and Multi-Pass Recognition
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Solution Overview
Problem
Information handling systems face challenges in processing and interpreting garbled text, which is unintelligible and difficult to parse, hindering efficient information processing and storage.
Innovation Solution
A method and system for parsing garbled text by receiving a corpus of text, normalizing characters, generating recognized words, and creating semi-structured text through Natural Language Processing, allowing for the calculation of word distributions and visual representation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional text parsing methods are used on garbled text, then processing time increases and accuracy decreases, but the system can still attempt to process the text
Solution Approach 1:
The patent segments the garbled text into character-level units and applies multiple parsing passes with different strategies. The text is divided into character sequences that are individually analyzed and recombined, allowing the system to handle uncertainty in word boundaries while maintaining processing efficiency through structured segmentation approaches.
Solution Approach 2:
The patent employs multiple parsing passes beyond a single attempt, including character-level parsing, word-level parsing, and phrase-level parsing. This excessive action ensures that even if earlier passes fail to fully resolve the garbled text, subsequent passes can recover additional meaningful information, thereby improving overall accuracy without requiring perfect first-attempt performance.
2Ease of operation
If garbled text is processed without preprocessing, then the system can maintain simple processing logic, but the text remains unintelligible and difficult to parse
Solution Approach 1:
The patent applies preliminary normalization and character-level parsing before attempting word-level recognition. This preliminary action prepares the garbled text by establishing character sequences and potential word boundaries, making the subsequent parsing operations more effective while maintaining a structured approach that doesn't overly complicate the overall process.
Solution Approach 2:
The patent introduces an intermediary character-level representation between the raw garbled text and final word recognition. This intermediate stage allows the system to work with a normalized form that preserves the original information while making it more amenable to subsequent parsing operations, effectively bridging the gap between unintelligible input and meaningful output.
3Measurement precision
If multiple parsing passes are applied to garbled text, then parsing accuracy improves, but system complexity increases
Solution Approach 1:
The patent segments the parsing process into distinct passes operating at different levels (character, word, phrase). Each pass handles specific aspects of the parsing task, allowing the system to improve accuracy through multiple specialized operations rather than a single complex monolithic parser, thereby managing system complexity through functional segmentation.
Solution Approach 2:
The patent employs a universal parsing framework that can operate at multiple levels (character, word, phrase) using the same basic parsing infrastructure. This multi-functionality allows the system to achieve high accuracy through adaptable parsing strategies without requiring entirely separate systems for each parsing level, reducing overall complexity while maintaining versatility.
Data Source
AI summary
A system, method, and computer-readable medium for performing a text parsing operation. The text parsing operation includes: receiving a corpus of text, at least a portion of the corpus of text comprising garbled text; parsing characters within the corpus of text to provide parsed characters from the corpus of text; parsing the parsed characters to generate recognized words from the parsed characters; generating semi-structured text from the recognized words; and, calculating a distribution of recognized words from the semi-structured text.