Unknown-box Approximation to Improve Optical Character Recognition Performance
This addresses accuracy issues in OCR for difficult and uncommon document domains, representing a domain-specific incremental improvement.
The paper tackles the problem of domain shift in optical character recognition (OCR) by proposing a novel approach to create a customized preprocessor for a specific OCR engine, which improved accuracy by up to 46% from the baseline in experiments.
Optical character recognition (OCR) is a widely used pattern recognition application in numerous domains. There are several feature-rich, general-purpose OCR solutions available for consumers, which can provide moderate to excellent accuracy levels. However, accuracy can diminish with difficult and uncommon document domains. Preprocessing of document images can be used to minimize the effect of domain shift. In this paper, a novel approach is presented for creating a customized preprocessor for a given OCR engine. Unlike the previous OCR agnostic preprocessing techniques, the proposed approach approximates the gradient of a particular OCR engine to train a preprocessor module. Experiments with two datasets and two OCR engines show that the presented preprocessor is able to improve the accuracy of the OCR up to 46% from the baseline by applying pixel-level manipulations to the document image. The implementation of the proposed method and the enhanced public datasets are available for download.