MLP NN for Numeral Recognition Using RGB Algorithm: OCR Metrics and Cross-Validation
Abstract
Handwritten numeral recognition remains a critical task in optical character recognition (OCR), with applications spanning from document digitization to automated data entry. This study explores a multi-layer neural network approach employing RGB algorithmic enhancements to enhance the accuracy of handwritten numeral recognition. Implemented in both Matlab and Python, the research investigates interface matching techniques and employs comprehensive OCR metrics for evaluation.
The study showcases significant advancements in understanding the intricacies of numeral character recognition, emphasizing the utilization of cross-validation techniques across Matlab and Python implementations. By validating findings across different datasets and experimental setups, the research establishes robustness and reliability in its methodology. Insights gleaned from this research not only contribute to the field of OCR but also lay a foundation for future advancements in handwriting recognition technologies.
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