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Handwriting Text Recognition (HTR) has been one of the main areas of research in machine learning and computer vision. Thus, the accuracy and efficiency of HTR systems have increased largely due to improvements in deep learning. This review paper casts insight into some of the research contributions regarding HTR, interlacing between different paradigms such as CNNs, RNNs, and transformer-based architectures. In addition to that, the study reported here offers a comparative view of the improvements surrounding the programming languages and machine learning libraries in terms of implementation of HTR, when related to current advancements and obstacles.
Keywords:
Handwritten Text Recognition, Deep Learning, Machine Learning, Optical Character Recognition (OCR), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), Transformer Models, Python, TensorFlow, PyTorch, OpenCV, Feature Extraction, Sequence Modeling, Document Digitization
Cite Article:
"A Review on Handwritten Text Recognition ", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 5, page no.a601-a608, May-2025, Available :http://www.ijrti.org/papers/IJRTI2505071.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator