Image-Based Document Language Identification via Script Classification
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Solution Overview
Problem
Existing methods struggle to accurately identify the language of text within image-based documents, as they are typically designed for text-based documents and fail to effectively process scanned or image-based inputs.
Innovation Solution
A cloud infrastructure node utilizes deep learning models to detect text lines, classify primary scripts, and determine languages through a pipeline comprising bbox detection, script classification, and intra-script language identification, employing models like DBNet, ResNet-18, CNN, RNN, and BiLSTM to process image-based documents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automatic language identification is implemented in image-based documents, then processing efficiency and accuracy are improved, but device complexity and computational resource requirements increase
Solution Approach 1:
The system segments the language identification process into distinct modules: image acquisition, text extraction (OCR), language detection, and confidence evaluation. Each module operates independently and can be optimized or replaced separately, reducing overall system complexity while maintaining high processing efficiency through automated pipeline execution.
Solution Approach 2:
The patent introduces an intermediary confidence evaluation mechanism that mediates between text extraction and final language determination. This intermediary layer assesses confidence scores and can request re-extraction or manual review when uncertainty arises, improving accuracy without requiring the entire system to be overly complex.
2Adaptability or versatility
If multiple languages are supported for automatic identification, then adaptability and versatility improve, but device complexity and processing time increase
Solution Approach 1:
The system changes parameters dynamically based on detected language characteristics. The language detection module analyzes textual features and adjusts extraction parameters accordingly, enabling efficient multi-language processing by adapting to each language's specific characteristics rather than using a fixed complex system for all languages.
Solution Approach 2:
The patent applies partial action by extracting and analyzing only the necessary textual features required for language identification rather than processing entire documents thoroughly. This selective approach enables rapid multi-language detection while maintaining sufficient accuracy, avoiding the time cost of exhaustive analysis.
3Measurement precision
If confidence thresholds are adjusted for different languages, then measurement precision improves, but device complexity increases
Solution Approach 1:
The system performs self-service by automatically adjusting confidence thresholds based on the detected language and document characteristics. Rather than requiring manual configuration for each language, the system self-calibrates its detection parameters, improving measurement precision while keeping the user interface simple and the configuration management automated.
Data Source
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AI summary
The present embodiments relate to identifying a native language of text included in an image-based document. A cloud infrastructure node (e.g., one or more interconnected computing devices implementing a cloud infrastructure) can utilize one or more deep learning models to identify a language of an image-based document (e.g., a scanned document) that is formed of pixels. The cloud infrastructure node can detect text lines that are bounded by bounding boxes in the document, determine a primary script classification of the text in the document, and derive a primary language for the document. Various document management tasks can be performed responsive to determining the language, such as perform optical character recognition (OCR) or derive insights into the text.