Publications

Handwritten text recognition in historical documents Thesis with the following contributions: Analysis of different neural network architectures and parameters Word segmentation using the output of the RNN layers CNN-based replacement of the RNN layers (enabling a purely convolutional architecture) Constrained CTC decoding algorithm (see paper for more details)

Word Beam Search: A Connectionist Temporal Classification Decoding Algorithm Paper presented at the 16th International Conference on Frontiers in Handwriting Recognition, 2018, Niagara Falls, USA. Properties of proposed algorithm: Decodes output of CTC-trained neural network Words constrained by dictionary Allows arbitrary number of non-word characters between words Optional word-level language model Faster than token passing

Aamir Reza, 20SCSE1010894

Faishal Sakil, 20SCSE1010877