Scanned Document Upload With Parallel OCR And Cloud Search
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Solution Overview
Problem
Conventional systems face limitations in scanning and uploading documents to cloud storage, as PDF images cannot be searched for text and are often restricted by size limits, requiring manual OCR processing and leading to incomplete uploads.
Innovation Solution
A system that allows users to select text or object recognition options before uploading, using a multipart upload process to divide and parallelize document images, reassembling them on the cloud and storing metadata or separate files with the processed data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a document is scanned to a PDF image, then the document can be stored in cloud storage, but the resulting PDF cannot be searched for text
Solution Approach 1:
The system performs OCR processing automatically during the upload workflow, converting scanned document images to searchable text format before final storage. This preliminary action ensures text searchability is achieved without requiring manual user intervention for OCR software execution.
2Reliability
If manual OCR processing is performed on scanned PDFs, then text searchability is achieved, but the process becomes time-consuming and complex
Solution Approach 1:
The system implements automated OCR processing that operates autonomously during the document upload process. The cloud-based service automatically detects scanned documents, performs optical character recognition, and generates searchable text versions without requiring users to manually execute OCR software or configure processing parameters.
Solution Approach 2:
OCR processing is performed automatically as part of the upload workflow, converting scanned document images to searchable text format before final storage. This preliminary action ensures text searchability is achieved without requiring manual user intervention for OCR software execution.
3Quantity of substance
If large document images are uploaded to cloud storage, then complete document storage is achieved, but cloud storage services impose size limits that prevent upload
Solution Approach 1:
The system automatically divides large document images into smaller segments or pages before upload, ensuring each segment falls within cloud storage size limits. This segmentation allows complete documents to be stored by breaking them into manageable parts that can be individually uploaded and then reassembled.
4Productivity
If document upload is performed sequentially, then upload completeness is achieved, but upload speed is slow
Solution Approach 1:
The system divides large document images into smaller segments before upload, enabling parallel processing of multiple segments simultaneously. This segmentation approach increases upload speed by allowing concurrent transfers while maintaining upload completeness through proper reassembly of all segments.
Solution Approach 2:
The system uploads document segments in parallel rather than sequentially, performing multiple upload operations simultaneously. This partial action approach (uploading parts concurrently) significantly increases overall upload speed while ensuring completeness through proper coordination and reassembly of all uploaded segments.
Data Source
AI summary
Systems, methods and products for uploading scanned documents for collaboration. A user chooses, prior to uploading a document, whether the user wishes for the document to be converted to a text-searchable PDF and/or processed to recognize objects in the image. When the document is scanned, it is uploaded to the cloud based system with the user-selected options, and the PDF/image is processed. The upload may be a multipart, parallel upload to increase the speed of the upload. When the PDF/image is received at the cloud based system, it is processed according to the user-selected options to generate metadata (text or recognized objects) which is stored with the uploaded document. The PDF/image and metadata are shared with users who can then search the metadata for the PDF/image.