Hybrid Architecture Pipeline for Cursive Handwriting Recognition

Artificial Intelligence

Cursive handwriting recognition, a difficult endeavor due to the complexity of the cursive script, requires the capacity to understand fully subtle patterns and variations. Despite machine learning and artificial intelligence advances, cursive handwriting detection accuracy still needs improvement, especially for complicated handwriting samples. This work offers a novel hybrid architecture pipeline for cursive handwriting recognition to address these problems and obtain state-of-the-art performance. A segmentation model, a bounding box sorting technique, and a recognition model are the three main components of the proposed pipeline. The first stage, the segmentation model, finds and segments individual words within the cursive text. The second stage is the bounding box sorting algorithm, which places the retrieved bounding boxes in the correct order, resembling the normal reading sequence. The final stage, the recognition model, uses a deep learning architecture with an Attention mechanism intended for cursive handwriting recognition to recognize the characters within each segmented word. Experiments with a benchmark dataset of cursive handwriting images show the pipeline's usefulness. The pipeline performs well when compared to existing state-of-the-art systems with an accuracy of 90%, proving its capacity to handle the complexity of cursive handwriting recognition. This pipeline provides an innovative and practical approach to cursive handwriting recognition, tackling the limitations of cursive script while producing cutting-edge results. It has the potential to be useful in a variety of sectors, including document processing, handwriting input for digital devices, and real-time text recognition. The pipeline's performance on large-scale datasets will be investigated in the future, as will its adaptation to multilingual cursive handwriting and the development of real-time implementation methodologies.

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