Recognising handwritten text is essential to automating a number of real-world tasks, including data entry, archive procedures, identity verification, and document digitisation. The variety of writing styles, inconsistent spacing, and distortions in handwritten inputs make it difficult to accurately translate handwritten content into machine-readable text. This research presents a deep learning-based system that integrates Recurrent Neural Networks (RNN), Bidirectional Long Short-Term Memory (BiLSTM), and Connectionist Temporal Classification (CTC) to tackle these issues. For the purpose of deciphering intricate sequences in cursive or unstructured handwriting, the RNN-BiLSTM framework allows the model to efficiently capture contextual relationships in both forward and backward temporal directions. Character segmentation during training is no longer necessary because of the incorporation of CTC decoding, which enables the model to learn from unsegmented sequences. To enhance the model’s generalisation across various writing styles, the system is trained using the IAM Handwriting Dataset, which comprises a diverse collection of natural handwriting examples. The suggested approach shows enhanced recognition accuracy and is particularly well-suited for real-world applications in fields like legal documents, secure identity systems, and extensive digitisation projects that demand accurate and effective text recognition.