ConvNeXt-Tiny Powered Deep Learning Framework for Automatic Grading of Handwritten Exams
To overcome the drawbacks of current techniques that usually concentrate on a single category and to tackle the difficult problem of identifying heterogeneous handwritten text in real-world situations like exam papers. A Handwritten Letter Classification Module therefore a Handwritten Text Intelligent Recognition Module are the two main parts of the system, and they operate together. The recognition module improves recognition accuracy by dynamically choosing specialised sub-networks for each category based on these findings. A Context-aware Recognition Optimisation Module is presented to further reduce mistakes resulting from identical character forms and different handwriting styles. This work creates a diverse, integrated handwritten text collection in recognition of the shortcomings of current public handwriting datasets, especially their lack of variation in character types and writing styles. The real-world applications, the dataset incorporates samples from several sources, such as English letters, numbers, and mathematical symbols. The proposed system employed the algorithm ConvNeXt-Tiny with maximum accuracy of 99.5 percentage.