Text Extraction from Images with Varying Orientations
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current OCR techniques fail to accurately extract text from images with varying orientations and complex backgrounds, requiring manual re-orientation and struggling to isolate text from overlapping background elements, leading to inefficiencies in data extraction from composite images.
Innovation Solution
A system utilizing custom deep neural networks for identifying, orienting, and extracting text from images, comprising modules for object detection, orientation correction, character classification, and formatting, which automatically identifies relevant text portions, corrects orientation, and separates text from complex backgrounds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If OCR techniques are used to extract text from images, then text extraction can be automated, but the techniques fail when images have varying orientations or complex backgrounds
Solution Approach 1:
The system segments the image processing task into distinct modules: orientation detection module that analyzes image orientation, cropping module that extracts relevant portions, and OCR module that performs text recognition. This segmentation allows each module to specialize in handling specific challenges like varying orientations and complex backgrounds independently.
Solution Approach 2:
The system performs preliminary orientation detection and image cropping before applying OCR techniques. By detecting the orientation of stamps or text elements first and pre-processing the image to correct orientation and isolate relevant regions, the subsequent OCR operation becomes reliable even when input images have varying orientations or complex backgrounds.
2Measurement precision
If manual re-orientation is performed to correct image orientation, then text extraction accuracy improves, but processing time increases significantly
Solution Approach 1:
The system replaces manual mechanical re-orientation operations with an automated orientation detection module that uses image analysis algorithms to detect stamp orientation and a cropping module that automatically corrects orientation. This substitution of manual mechanical processes with automated computational processes maintains high text extraction accuracy while dramatically reducing processing time.
3Productivity
If OCR techniques are applied to images with complex backgrounds, then text extraction can proceed, but the techniques fail to isolate text from overlapping background elements
Solution Approach 1:
The system applies local quality enhancement by detecting the specific orientation and position of stamps or text elements within the image and applying targeted cropping and orientation correction only to those specific regions. This localized processing isolates the relevant text from complex backgrounds while maintaining high productivity by avoiding unnecessary processing of entire images.
Data Source
AI summary
A system for extracting text from images comprises a processor configured to receive a digital copy of an image and identify a portion of the image, wherein the portion comprises text to be extracted. The processor further determines orientation of the portion of the image, and extracts text from the portion of the image considering the orientation of the portion of the image.