ABSTRACT
This research paper based on an innovative idea that depict for handwritten text recognition, combine with the Convolutional Recurrent Neural Networks (CRNNs) into its architecture. Comprising two primary modules, namely text detection and text recognition, the system manages a sophisticated approach towards decode handwritten text from image. The text detection component employs a fusion of an advanced image processing techniques and highly developed for object identification models to effectively find out handwritten text regions within diverse image datasets. Following text detection, the text recognition module controls the power of CRNNs to precisely recognize and transcribe an identified text segment. The resilience and efficiency of the system are fully proven by rigorous review and extensive experimentation across multiple datasets. These results focus on how it much changes the traditional document processing operations and shows some exciting opportunities for automation and optimization across a range of disciplines, which are based on the interpretation of handwritten text. To summarize, this study offers a impressive accurate progress in automated handwritten text identification for cursive handwriting, demonstrating a workable method that can enhance the work related to the document analysis and management.
