Handwriting Recognition Based on Temporal Order Restored by the EndtoEnd System In this paper, we present an original framework for offline handwriting recognition. Our developed recognition system is based on Sequence to Sequence model employing the encoder decoder LSTM, for recovering temporal order from offline handwriting. Handwriting temporal recovery consists of two parts which are respectively extracting features using a Convolution Neural Network (CNN) followed by an LSTM layer and decoding the encoded vectors to generate temporal information using BLSTM. To produce a humanlike velocity, we make a Sampling operation by the consideration of trajectory curvatures. Our work is validated by the LSTM recognition system based on Beta Elliptic model that is applied on Arabic and Latin OnOff dual handwriting character database.

Evaluating SequencetoSequence Models for Handwritten Text Recognition Encoderdecoder models have become an effective approach for sequence learning tasks like machine translation, image captioning and speech recognition, but have yet to show competitive results for handwritten text recognition. To this end, we propose an attentionbased sequencetosequence model. It combines a convolutional neural network as a generic feature extractor with a recurrent neural network to encode both the visual information, as well as the temporal context between characters in the input image, and uses a separate recurrent neural network to decode the actual character sequence. We make experimental comparisons between various attention mechanisms and positional encodings, in order to find an appropriate alignment between the input and output sequence. The model can be trained endtoend and the optional integration of a hybrid loss allows the encoder to retain an interpretable and usable output, if desired. We achieve competitive results on the IAM and ICFHR2016 READ data sets compared to the stateoftheart without the use of a language model, and we significantly improve over any recent sequencetosequence approaches.