Improved Handwritten Document Processing with Integrated Spelling and Grammar Correction in Deep Learning

This paper proposes an advanced handwritten document processing system, which is designed to enhance the handwritten text recognition by jointly using a Convolutional Neural Networks and Auto-Encoding Transformer models with BERT-based spelling correction and GECToR-based grammatical correction, and is further compared to an existing OCR-based approach for handwritten recognition. Materials and Methods: The results of this study consist of two groups: Group 1 consists of an experimental group that used the already- existing OCR system, which was tested against ten test samples, averaging about 78% accuracy in recognition. Group 2 presents the proposed CNN + Auto-Encoding Transformer model integrated with BERT and GECToR. Testing criteria included accuracy, error rate, and processing time. Sample size in both groups was determined through prior studies. It was expected to have a test power of 80%, with a significance level of 0.05 and a 95% confidence interval. Statistical analysis was done using SPSS 26.0 software; for comparing two independent samples, the appropriate statistical tool is the independent samples t-test. Result: The results show clearly that the proposed approach of utilizing the CNN Auto Encoding Transformer performs much better than a traditional approach of utilizing an Optical Character Recognition (OCR) system. The proposed approach achieves high accuracy of 93.6%, reduces errors to 0.11, and also speeds up recognizing handwritten text in less than 0.58 seconds while maintaining statistically significant improvements to confidence level of (p < 0.001). Conclusion: Overall, the combined effect of the CNN + Auto-Encoding Transformer with the use of BERT and GECToR makes the recognition of the handwritten text much more precise and even easier to read. The system is faster compared to the traditional OCR methods.