Every day, millions of handwritten notes often record valuable knowledge, critical insights, and critical information. Given the importance of handwritten notes, an effective method to digitize, store, and extract information from these notes would not only greatly enhance the ability to securely preserve and manage information but also unlock a variety of real-world applications. However, handwriting recognition still remains a challenging within Computer Vision (CV) and Artificial Intelligence (AI). Although various studies in Optical Character Recognition (OCR) have achieved remarkable success in recognizing handwritten text at the character level, they still struggle with overlapping characters, inconsistent spacing, and the wide variety of handwriting styles encountered in practice. To address these limitations, this research proposes a Handwritten Word Recognition (HWR) model with a two-stage pipeline, specifically optimized for handwritten English notes. The first stage focuses on accurately detecting the location of each word in an image. The second stage utilizes ResUNet-101 for feature extraction, followed by Bi-LSTM and CTC decoding to recognize entire words. Finally, a post-processing phase leverages Natural Language Processing (NLP) techniques to enhance accuracy further. The proposed method achieves an accuracy of 77% on testing data, demonstrating significant improvements over traditional OCR systems in handling handwritten word. This research not only advances handwritten note digitization but also lays the groundwork for automated information extraction systems, benefiting fields such as education, research, and document archiving.