EXTENSION FOR HANDWRITTEN CHARACTER RECOGNITION USING RNN-GRU ALGORITHM

Recognizing handwritten characters is a complex task in pattern recognition and computer vision, demanding advanced techniques to enable computers to discern and transform handwritten or printed characters into a digital format. The integration of the GRU algorithm within the RNN-CNN hybrid methodology marks a significant advancement in handwritten character recognition. By transitioning from a character-by-character to a line-by-line recognition approach, the system aims to improve the conversion of handwritten text into digital formats. GRU enhances context awareness by predicting subsequent letters in incomplete words, thereby facilitating more accurate recognition. Leveraging the IAM dataset not only provides ample training data but also aids in error detection and correction, enhancing the overall accuracy and reliability of the system. This innovative approach underscores a commitment to advancing OCR capabilities beyond isolated characters and words, toward more comprehensive and context-aware text recognition, thereby paving the way for more effective digitization of handwritten documents.