Query-Guided Image Text Prediction for Targeted OCR Extraction
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Solution Overview
Problem
Existing optical character recognition (OCR) systems face challenges in accurately detecting and recognizing text in real-world images due to unpredictable text locations, noise, and the need to extract specific elements of interest rather than all text, especially in complex scenes like streets or documents.
Innovation Solution
A system that integrates text detection and character recognition in a single end-to-end process, using feature extraction and decoder modules guided by queries to predict specific text elements of interest, employing neural networks for orientation-invariant text recognition and refining outputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If traditional image processing techniques are used to extract text, then the process requires manual intervention and is time-consuming, but automation reduces productivity and increases errors
Solution Approach 1:
The system enables self-service by automatically detecting, segmenting, and recognizing text in images without requiring manual intervention. The automated text extraction system processes images independently, eliminating the need for human operators to manually transcribe or transcribe text, thereby achieving both automation and high productivity simultaneously
Solution Approach 2:
The patent replaces manual mechanical text extraction processes with an automated computational system. Instead of human operators physically reading and transcribing text, the system uses image processing algorithms, optical character recognition (OCR), and machine learning models to automatically extract text information, substituting mechanical human labor with automated digital processing
2Measurement precision
If manual text extraction is performed, then accuracy can be maintained, but time consumption increases and productivity decreases
Solution Approach 1:
The system performs preliminary actions by pre-processing images to enhance text visibility and clarity before extraction. This includes image normalization, contrast enhancement, and noise reduction techniques that prepare the text data in advance, ensuring high extraction accuracy while reducing the time required for actual text recognition and transcription
Solution Approach 2:
The automated system incorporates feedback mechanisms where the extracted text is validated and corrected automatically. The system can compare extracted text against expected patterns, detect errors, and self-correct, maintaining high accuracy levels while processing images much faster than manual extraction, thereby resolving the contradiction between accuracy and time consumption
3Productivity
If automated text extraction systems are implemented, then productivity increases, but system complexity and computational resources are required
Solution Approach 1:
The system applies segmentation by dividing the complex text extraction process into distinct modular stages: image preprocessing, text detection, character segmentation, OCR recognition, and post-processing. This segmentation allows each component to be optimized independently and simplifies the overall system architecture, making the high-productivity automated system more manageable and less complex
Solution Approach 2:
The patent implements a universal text extraction system that can handle multiple image types, languages, and text formats within a single integrated framework. The system uses multi-functional algorithms that can process various document types (scan, photograph, handwritten text) and languages simultaneously, reducing the need for multiple specialized systems and thereby reducing overall complexity while maintaining high productivity
Data Source
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AI summary
Systems and methods for detecting and predicting text within images. An image is passed to a feature-extraction module. Each image typically contains at least one text object, and each text object contains at least one character. Based on the image, the feature-extraction module generates at least one feature map indicating text object(s) in the image. The feature map(s) is then passed to a decoder module. In some implementations, the decoder module applies a weighted mask to the feature map(s). Based on the feature map(s), the decoder module predicts a sequence of characters in the text object(s). In some embodiments, that prediction is based on previous known data. The decoder module is directed by a query that indicates at least one desired characteristic of the text object(s). An output module then refines the predicted content. At least one neural network may be used.