Log Linear Model for OCR Feature Weight Optimization
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Solution Overview
Problem
Current optical character recognition (OCR) systems face challenges in accurately processing text images from diverse sources, including varying languages and writing systems, due to limitations in combining disparate feature functions and adapting to different input images.
Innovation Solution
The implementation of a log linear model that evaluates feature functions to determine feature values for text images, identifies candidate characters using a feature table with co-occurrence counts and language models, and applies a search function to minimize transcription costs through a beam search algorithm, allowing for the combination of multiple feature functions and adaptation to different languages and writing systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional OCR methods are used to process text images, then the system structure is simple, but the accuracy and adaptability to different languages and writing systems deteriorates
Solution Approach 1:
The text image is divided into multiple image segments, each representing a character or character component. This segmentation allows the system to process complex text images by breaking them down into manageable units, improving accuracy while maintaining computational feasibility
Solution Approach 2:
The log linear model and feature table are designed to be universal across different languages and writing systems. The system uses language models and feature functions that can adapt to various scripts without requiring completely separate processing pipelines, enhancing both accuracy and versatility
Solution Approach 3:
The system dynamically adjusts feature weights and model parameters based on the input text image characteristics. By changing parameters such as feature weights in the log linear model and adapting to different language models, the system achieves high accuracy across diverse languages and writing systems
2Measurement precision
If multiple feature functions are combined to improve OCR accuracy, then the measurement precision improves, but the device complexity increases
Solution Approach 1:
Multiple feature functions are merged into a unified log linear model framework. The system combines various features (pixel-based, structural, contextual) into a single probabilistic model that evaluates candidate characters simultaneously, improving accuracy while managing complexity through unified processing
Solution Approach 2:
The feature table acts as an intermediary between image segments and candidate characters. It stores pre-computed co-occurrence counts and probabilities, mediating the complex interactions between multiple feature functions and enabling efficient accuracy improvement without proportional complexity increase
3Measurement precision
If a comprehensive feature table with co-occurrence counts is used to identify candidate characters, then the recognition accuracy improves, but the loss of time increases
Solution Approach 1:
The feature table is pre-computed during an offline training phase, storing co-occurrence counts and probabilities before actual OCR processing. This preliminary action allows the online system to quickly retrieve pre-computed information, improving recognition accuracy without incurring computational overhead during text processing
Solution Approach 2:
The system uses a beam search algorithm that explores only the most promising candidate characters rather than exhaustively evaluating all possibilities. By performing partial action on the most likely candidates identified through the feature table, the system achieves high accuracy while limiting processing time
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for performing optical character recognition. In one aspect, a method includes receiving a text image I. A set of feature functions are evaluated for a log linear model to determine respective feature values for the text image I, wherein each feature function hi maps the text image I to a feature value, and wherein each feature function hi is associated with a respective feature weight λi. A transcription {circumflex over (T)} is determined that minimizes a cost of the log linear model.