Abstract
These days a large number of handwritten documents are available in scanned images. The aim of Handwritten Text Recognition (HTR) is to transcribe the offline document images by a computer through the use of deep neural networks. Even though hybrid architectures, designed for this purpose are gaining popularity, we aim to further optimize the prescribed models by changing the hyperparameters associated with the number of convolutional neural network layers on the overall accuracy. We explore the implementation of hybrid architectures and show that mainly the depth of the network plays a noteworthy role in improving the accuracy of the trained model.
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Goel, C., Chaudhary, A., Indu, S., Majumdar, S. (2021). Deep Neural Network Optimization for Handwritten Text Recognition. In: Panigrahi, C.R., Pati, B., Pattanayak, B.K., Amic, S., Li, KC. (eds) Progress in Advanced Computing and Intelligent Engineering. Advances in Intelligent Systems and Computing, vol 1299. Springer, Singapore. https://doi.org/10.1007/978-981-33-4299-6_7
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DOI: https://doi.org/10.1007/978-981-33-4299-6_7
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