Document Image Quality Scoring for Accurate Automated Categorization
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Solution Overview
Problem
Existing document processing systems face inefficiencies in handling documents of varying quality, leading to wasted computational resources and inaccurate results due to the need for manual review and processing of low-quality images.
Innovation Solution
An automated tool that assesses document quality using an OCR algorithm and a clean score, determining if the document is of sufficient quality before applying machine learning algorithms, and rejecting or queuing low-quality documents for manual review.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated processing is applied to all documents regardless of quality, then processing speed increases, but computational resources are wasted on low-quality documents
Solution Approach 1:
The system performs a preliminary quality assessment of documents using image processing algorithms before attempting automated processing. This preliminary action filters out low-quality documents that would waste computational resources, while allowing high-quality documents to proceed to automated processing, thus resolving the contradiction between processing speed and resource waste.
Solution Approach 2:
An intermediary quality assessment module is introduced between document reception and automated processing. This intermediary component evaluates document quality metrics (such as image clarity, text legibility, and document completeness) and acts as a gatekeeper, directing only suitable documents to the automated processing pipeline while routing unsuitable documents for manual review or rejection, thereby preventing computational resource waste.
2Reliability
If manual review of all documents is performed, then processing accuracy is maintained, but processing time and labor requirements increase
Solution Approach 1:
The system applies different processing approaches to different documents based on their quality characteristics. High-quality documents receive automated processing for speed, while low-quality documents receive manual review for accuracy. This local differentiation of processing quality matches the varying quality of input documents, resolving the contradiction between overall processing time and individual document accuracy.
Solution Approach 2:
The system incorporates feedback mechanisms where manually reviewed documents can be used to retrain and improve the automated processing algorithms. The quality assessment results feed back into the system to refine future automated processing, gradually improving accuracy while maintaining high processing speeds for the majority of documents.
3Productivity
If low-quality documents are processed automatically, then processing throughput increases, but data quality and reliability decrease
Solution Approach 1:
The system performs preliminary quality assessment before automated processing to ensure that only documents meeting minimum quality thresholds are processed automatically. This preliminary filtering action maintains data quality by preventing poor-quality images from entering the processing pipeline, while still allowing high-throughput processing of the qualified documents.
Solution Approach 2:
The system changes the parameter of document quality threshold before automated processing. By adjusting the minimum quality threshold parameter, the system can control the balance between throughput and data quality. Documents above the threshold proceed to automated processing for high throughput, while documents below the threshold are flagged for manual review to maintain data quality standards.
Data Source
AI summary
An apparatus includes a memory and a processor. The memory stores a dictionary and a machine learning algorithm trained to classify text. The processor receives an image of a page, converts the image into a set of text, and identifies a plurality of tokens within the text. Each token includes one or more contiguous characters that are both preceded and followed by whitespace within the text. The processor identifies invalid tokens by removing tokens of the plurality of tokens that correspond to words of the dictionary. The processor calculates, based on a ratio of a total number of valid tokens to a total number of tokens, a score. In response to determining that the score is greater than a threshold, the processor applies the machine learning algorithm to classify the text into a category and stores the image and/or text in a database according to the category.