Handwriting Recognition With Selective Language Model Refinement
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Solution Overview
Problem
Existing handwriting recognition systems struggle with historical documents due to degraded records, language changes, varied handwriting styles, and spelling errors, leading to inaccurate transcriptions and high labor costs.
Innovation Solution
Incorporating a character-based language model into handwriting recognition systems to refine predictions by leveraging knowledge of spoken and written language, using a loss prediction module to determine when to apply the language model for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a language model is integrated into the handwriting recognition system to improve accuracy, then the robustness and word accuracy are improved, but the device complexity and computational resources increase
Solution Approach 1:
The system performs preliminary loss prediction using a lightweight module before deciding whether to invoke the full language model. This preliminary action filters out cases that don't require complex processing, thereby reducing overall system complexity while maintaining robustness where needed.
Solution Approach 2:
The handwriting recognition system is segmented into distinct components: a base recognition model, a loss prediction module, and a language model. This segmentation allows selective application of computational resources, where the language model is only activated when the loss prediction indicates potential errors, thus managing device complexity effectively.
2Measurement precision
If a language model is used to refine handwriting predictions, then the word accuracy is improved, but the computational resources and processing time increase
Solution Approach 1:
Instead of applying the language model to all predictions, the system uses partial action by selectively applying it only to cases where the loss prediction module indicates potential errors. This reduces computational resource consumption while maintaining high word accuracy where it matters most.
Solution Approach 2:
The loss prediction module performs a preliminary assessment before the language model is invoked. This preliminary filtering action identifies only those predictions that benefit from language model refinement, thereby optimizing computational resource usage while preserving word accuracy.
3Measurement precision
If the system processes all handwriting predictions through the language model, then the word accuracy is maximized, but the productivity and processing speed decrease
Solution Approach 1:
The system applies partial action by processing only a subset of predictions through the language model - specifically those flagged by the loss prediction module. This maintains high word accuracy for problematic cases while preserving processing speed for the majority of straightforward predictions.
Solution Approach 2:
The processing pipeline is segmented into fast path (direct output for high-confidence predictions) and slow path (language model refinement for uncertain predictions). This segmentation enables the system to maintain high productivity for most cases while achieving maximum word accuracy when needed.
4Reliability
If the language model is applied to correct spelling errors and unrecognized words, then the robustness is improved, but the device complexity increases
Solution Approach 1:
The loss prediction module performs a preliminary screening to identify predictions that may contain spelling errors or unrecognized words. This preliminary action enables the robustness improvement from the language model to be applied only where necessary, thereby limiting the increase in device complexity.
Solution Approach 2:
The loss prediction module acts as an intermediary between the base recognition model and the language model. It mediates by filtering and directing only appropriate cases to the language model, thus enabling robustness improvement while controlling device complexity through selective intermediation.
Data Source
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AI summary
Systems and methods for handwriting recognition using language modeling facilitate improved results by using a trained language model (276) to improve results from a handwriting recognition machine learning model (204). The language model (276) may be a character-based language model trained on a dataset pertinent to field values on which the handwriting recognition model (204) is to be used. A loss prediction module (256) may be trained with the handwriting recognition model (204) and/or the language model (276) and used to determine whether a prediction (210) from the handwriting recognition model (204) should be refined by passing the prediction (210) through the trained language model (276).