Visalakshi Annepu

Papers

Total Citations

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About

Dr. Visalakshi Annepu is a leading researcher in the field of handwritten text recognition (HTR), with a focused expertise in deep learning architectures for complex pattern analysis. Her most impactful work, "Enhancing handwritten text recognition accuracy with gated mechanisms" (2024, 15 citations), addresses one of the most persistent challenges in document analysis: the accurate transcription of highly variable and structurally complex handwritten scripts. Dr. Annepu’s key contribution lies in her innovative application of gated mechanisms, particularly Long Short-Term Memory (LSTM) networks, to significantly improve the robustness of HTR systems against distortions and stylistic variations. By demonstrating how these architectures can selectively retain and forget contextual information, her research has provided a practical pathway to higher recognition accuracy, directly benefiting fields from historical manuscript digitization to automated form processing. Her work stands as a notable achievement in bridging the gap between theoretical sequence modeling and real-world document understanding, establishing her as a rising authority in the intersection of computer vision and natural language processing.