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Handwritten Text Recognition Remains A Challenging Problem In The Field Of Computer Vision And Pattern Recognition Due To The High Variability In Individual Writing Styles, Distortions, Overlapping Characters, And Background Noise. Traditional Recognition Systems Based On Handcrafted Features And Rule-based Methods Often Fail To Achieve High Accuracy And Generalization In Real-world Scenarios. Recent Advancements In Deep Learning Have Introduced Powerful Hybrid Models That Combine Convolutional Neural Networks (CNNs) And Recurrent Neural Networks (RNNs), Forming Convolutional Recurrent Neural Networks (CRNNs). CNNs Are Effective In Extracting Robust Spatial Features From Handwritten Images, While RNNs—particularly Long Short-Term Memory (LSTM) Or Gated Recurrent Units (GRUs)—capture The Sequential Dependencies Inherent In Handwriting. This Study Focuses On Developing A CRNN-based Framework For End-to-end Handwritten Text Recognition, Eliminating The Need For Manual Feature Engineering. The Model Is Trained On Large-scale Handwritten Datasets And Evaluated Using Sequence-to-sequence Mapping With Connectionist Temporal Classification (CTC) Loss To Align Input Images With Output Text. Experimental Results Demonstrate That The Proposed CRNN Architecture Achieves High Recognition Accuracy And Robustness Against Diverse Handwriting Variations, Making It A Promising Solution For Applications Such As Digitizing Historical Documents, Automated Form Processing, And Intelligent Handwriting-based Interfaces.