Context-Specific Dictionary for OCR Text Recognition
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Solution Overview
Problem
Conventional Optical Character Recognition (OCR) processes face inaccuracies due to limitations in image quality and context, leading to errors in text recognition from digital photos and video frames, especially when using camera feeds from mobile devices, which are prone to glare, skewed text, and occlusions.
Innovation Solution
The use of context-specific dictionaries and scene categorization to improve OCR accuracy by identifying the context of the image data, such as menus, maps, or documents, and utilizing additional frames for enhanced classification and error correction, allowing for more accurate text recognition and post-processing enhancements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional OCR processes are used on image data from mobile devices, then text recognition can be performed, but accuracy deteriorates due to image quality issues such as glare, skewed text, and occlusions
Solution Approach 1:
The system performs scene categorization and identifies contextual information from the image data before performing OCR. By determining the type of scene (e.g., document, book, menu, billboard) and extracting relevant contextual features in advance, the system prepares appropriate correction strategies and vocabulary lists that are specific to each scene type, thereby improving OCR accuracy while accounting for scene-specific image quality issues
Solution Approach 2:
The system uses identified scene context and extracted text to generate corrections for OCR errors. By comparing OCR results against scene-specific vocabulary lists and contextual information, the system identifies and corrects recognition errors, then feeds these corrections back to improve the overall text recognition accuracy for that scene type
2Measurement precision
If general language dictionaries are used for OCR correction, then broad language coverage is achieved, but accuracy deteriorates due to lack of context-specific vocabulary
Solution Approach 1:
The system generates and uses vocabulary lists that are specific to each identified scene type (e.g., document-specific, book-specific, menu-specific vocabulary). By tailoring the vocabulary and correction rules to the local context of each scene type rather than using a single general dictionary, the system improves text recognition accuracy for context-specific terms and phrases
Solution Approach 2:
The system dynamically adjusts the vocabulary and correction parameters based on the identified scene type. By changing the linguistic parameters and vocabulary scope to match the specific context (e.g., formal language for documents, casual language for social media posts), the system achieves both accuracy and adaptability across different text types
3Measurement precision
If scene categorization and context-specific processing are implemented, then text recognition accuracy is improved, but system complexity increases
Solution Approach 1:
The system divides the text recognition process into distinct segments: scene categorization, context extraction, OCR processing, and context-specific correction. By segmenting the overall process into manageable stages that can be independently optimized and executed, the system improves accuracy through specialized processing while keeping the overall system complexity manageable through modular design
Data Source
AI summary
Disclosed are techniques for recognizing text from one or more frames of image data using contextual information. In some implementations, image data including a captured textual item is processed to identify an entity in the image data. A context can be selected using the entity, where the context corresponds to a dictionary. Text in the captured textual item can be identified using the dictionary. The identified text can be output to a display device.