Multilingual Document OCR Segmentation for Bulk Upload Processing

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Solution Overview

Problem

Existing OCR systems are slow and inaccurate when processing bulk uploaded documents containing multiple languages, requiring manual intervention to improve performance.

Innovation Solution

A method and system that splits documents into sections based on language, performs OCR on each section with a single language setting, and combines the results to restore the original document, using embedded identifiers for reassembly.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Quantity of substance

If OCR is performed on multiple documents uploaded in bulk, then the processing quantity increases, but the processing speed becomes slow and accuracy decreases

Engineering Contradiction:

Improvenumber of documents processedVSAvoidprocessing speed

Core Design Contradiction:

Quantity of substanceVSProductivity

Solution Approach 1:

The patent segments multilingual documents into separate single-language sections based on language detection. Each section is processed independently with optimized language-specific OCR settings, allowing parallel processing and improving overall throughput while maintaining accuracy. This segmentation resolves the contradiction by enabling bulk processing without sacrificing speed or accuracy.

Inventive Principle:

Principle #1Segmentation

2Quantity of substance

If OCR is performed on multiple documents uploaded in bulk, then the processing quantity increases, but the conversion accuracy becomes low

Engineering Contradiction:

Improvenumber of documents processedVSAvoidconversion accuracy

Core Design Contradiction:

Quantity of substanceVSMeasurement precision

Solution Approach 1:

The patent applies local quality by detecting language-specific sections within documents and applying optimized OCR parameters tailored to each language. Instead of using a single universal OCR setting for all documents, the system adapts language-specific parameters (such as character set, recognition model, and post-processing rules) to each section, thereby maintaining high accuracy across multilingual bulk processing.

Inventive Principle:

Principle #3Local quality

3Measurement precision

If manual intervention is used to improve OCR performance, then the accuracy can be optimized, but the automation level decreases

Engineering Contradiction:

ImproveOCR accuracyVSAvoidmanual intervention required

Core Design Contradiction:

Measurement precisionVSExtent of automation

Solution Approach 1:

The patent implements self-service by automatically detecting languages in uploaded documents, segmenting them into appropriate sections, and applying optimized OCR settings without requiring manual user intervention. The system autonomously performs language identification, document segmentation, and parameter selection based on pre-defined language models, thereby maintaining high accuracy while maximizing automation.

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS20250391194A1System and methods for managing uploaded document

Publication Date: 2025.12.25 KYOCERA DOCUMENT SOLUTIONS INC

AI summary

A bulk of electronic documents are uploaded to a document management system. A document managing module within the document management system detects if a uploaded document contains distinct sections, each of which contains substantially one single language. If the distinct sections can be separated in a clean manner, the module divides the uploaded document into multiple files based on the multiple languages in the distinct sections, each of the multiple files contains a single language. The multiple files are then processed with OCR operations to generate multiple sectioned PDF documents. All the multiple sectioned PDF sections are then combined together to restore the original uploaded document in a searchable PDF form.