OCR Language Setting for Bulk Multilingual Document Uploads
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Solution Overview
Problem
Existing OCR systems are inefficient and inaccurate when processing bulk imported multilingual documents, requiring manual intervention to adjust language settings.
Innovation Solution
A computer-implemented method that automatically determines OCR language settings by analyzing a sample document, adjusting settings based on accuracy thresholds, and applying these settings to other documents in the batch, with manual intervention as a fallback for persistent inaccuracies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If OCR is performed on multiple documents uploaded in bulk using traditional methods, then processing can be completed, but the processing speed is slow and accuracy is low
Solution Approach 1:
The system performs preliminary language detection on a sample document from the batch before executing OCR on all documents. By identifying the language(s) present in advance, the system can configure appropriate OCR parameters and dictionaries beforehand, avoiding repeated language detection and parameter adjustment during the OCR process, thus improving processing speed while maintaining accuracy.
Solution Approach 2:
The system uses a sample document (a copy or representative of the batch) to determine language settings and OCR parameters. The language detection and parameter optimization performed on this sample document are then applied to all documents in the batch, eliminating the need to process each document individually for language identification, thereby significantly improving processing efficiency.
2Reliability
If manual intervention is used to adjust language settings for each document, then OCR accuracy can be improved, but processing time increases significantly
Solution Approach 1:
The system automatically detects languages and configures OCR parameters without requiring manual intervention. The language detection module autonomously identifies the language(s) in the uploaded documents and adjusts OCR settings accordingly, enabling the system to serve itself and eliminating the time-consuming manual configuration process while maintaining high accuracy.
Solution Approach 2:
The system implements a feedback mechanism where OCR results are evaluated for accuracy, and if the accuracy meets predetermined thresholds, the language settings and parameters are confirmed. If accuracy is insufficient, the system automatically adjusts parameters and re-runs OCR, creating a closed-loop feedback system that improves accuracy without requiring manual intervention for each document.
3Reliability
If OCR language settings are customized for each document individually, then accuracy improves, but device complexity and processing overhead increase
Solution Approach 1:
The system uses a unified language detection and parameter configuration module that serves all documents in the batch. By detecting the language once for the entire batch (or for a representative sample) and applying the same OCR parameters universally, the system avoids the complexity of individual document processing while maintaining accuracy through intelligent batch-level language identification.
Solution Approach 2:
The system merges the language detection and parameter configuration steps into a single preliminary process that applies to the entire document batch. Instead of separately detecting languages and configuring parameters for each document, the system combines these operations at the batch level, reducing system complexity and processing overhead while maintaining the ability to handle multilingual documents accurately.
Data Source
AI summary
A document management system and method are disclosed. A bulk of electronic documents are uploaded to the document management system. An OCR language setting module is provided within the document management system. The OCR language setting module performs a first OCR operation on a first document of the bulk of electronic documents using a first language, and compares an accuracy level of the OCR performance with a preset threshold level. If the accuracy meets the preset threshold level, the first language will be set as the OCR language settings. This OCR language settings will be used to perform OCR operations on all remaining documents of the bulk of electronic documents. If the accuracy level of the first OCR operation does not meet the threshold level, the system runs a second OCR operation on the first document using a second language.