Document Orientation Detection Using Adaptive Model Selection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional image processing technologies face challenges in accurately determining the orientation of documents with mixed content, such as photographs and text, especially when characters are small, leading to reduced accuracy and increased memory consumption.
Innovation Solution
An image processing apparatus and method that determines the document type based on character and non-character areas, selects appropriate models for orientation correction, and applies either reduction or partial image processing to improve orientation determination accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If OCR-based top-bottom determination is used for documents with small characters, then the determination can be performed, but the accuracy is reduced and memory consumption increases
Solution Approach 1:
The patent divides the document image into multiple regions (character regions and non-character regions) and processes each region separately using different determination methods. This segmentation allows the system to avoid applying resource-intensive OCR to the entire image, thereby reducing memory consumption while maintaining accuracy in character region determination.
Solution Approach 2:
The patent applies different processing qualities to different regions of the image. Character regions are processed with high precision using OCR, while non-character regions use simpler determination methods. This local quality approach optimizes the balance between accuracy and resource usage, reducing overall memory consumption while maintaining determination accuracy where it matters most.
2Measurement precision
If uniform preprocessing is applied to all document types, then the processing is simple, but the top-bottom determination accuracy is reduced for specific document types
Solution Approach 1:
The patent implements a dynamic preprocessing system that automatically adjusts the processing method based on the detected document type. The system first performs initial analysis to classify the document, then dynamically selects and applies the most appropriate preprocessing method for that specific type, optimizing accuracy without requiring manual configuration.
Solution Approach 2:
The patent changes processing parameters based on document type classification. Different document types (e.g., text-heavy, image-heavy, mixed-content) trigger different preprocessing parameter settings, including varying levels of image reduction, filtering, and region-of-interest selection, thereby optimizing determination accuracy for each document type.
3Measurement precision
If the entire input image is used for top-bottom determination, then all information is available, but the processing time and memory consumption increase
Solution Approach 1:
The patent extracts and focuses processing on the most informative regions of the image by identifying character regions and non-character regions separately. By taking out only the relevant portions for detailed analysis and using simpler methods for other areas, the system reduces processing time and memory consumption while maintaining determination accuracy.
Solution Approach 2:
The patent applies partial action by performing comprehensive processing only on critical regions (such as character regions for orientation cues) while using simplified processing for other areas. This selective approach reduces overall processing time and memory usage while maintaining sufficient accuracy for top-bottom determination.
Data Source
AI summary
An image processing apparatus includes circuitry to determine a type of a document based on a determination result of a character area and a non-character area in an input image of the document; select a model to be used in top-bottom determination from a plurality of models based on the type of the document; reduce the input image, to generate a reduced image; and cut out a part of the input image as a partial image. The circuitry outputs a top-bottom determination result of the input image using the selected model and one of the reduced image and the partial image corresponding to the model.