Authors: F. Zamora-Martínez, V. Frinken, S. España-Boquera, M.J. Castro-Bleda, A. Fischer, H. Bunke Source: Pattern Recognition, Volume 47, Issue 4, April 2014, Pages 1642–1652 Reporter: Chia-Chin Chang, Yu-Wen, Lo Date: 2014 / 5 / 20 1 Neural network language models for off-line handwriting recognition

Outline 2 Introduction Method Language modeling with neural networks Recognition systems Experiment & Result Conclusion

Introduction 3 Off-line handwritten text recognition (HTR) is the handwritten text into a machine-readable of that text. Current best practice is still to use back-off N-gram language models estimated from large corpora in HTR. In this paper, the current best recognition results for the IAM off-line database. The authors use two recognition systems – Bidirectional Long Short-Term Memory (BLSTM) Hybrid Hidden Markov Models (Hybrid HMM)

Method 4 Language modeling with neural networks(1/4)

Method 5 Language modeling with neural networks(2/4)

Method 6 Language modeling with neural networks(3/4) By a pre-computed table which stores the distributed encoding of each word and is computed as

Method 7 Language modeling with neural networks(4/4) The hidden layer of the NN LM, denoted as H, computes The output layer O has |Ω| units, one for each word of the vocabulary.

Method 8 Recognition systems BLSTM neural network recognizer Hybrid HMM and neural network recognizer Combination of two recognizer (ROVER recognizer)

Method 9 BLSTM neural network recognizer(1/2) A sequential representation of a normalized text line using 9 geometric features.

Method 10 BLSTM neural network recognizer(2/2)

Method 11 Hybrid HMM and neural network recognizer

Method 12 ROVER recognizer

Experiment & Result(1/9) 13 Handwriting and LM databases Examples of line images from the IAM-DB

Experiment & Result(2/9) 14 Handwriting and LM databases IAM off-line handwriting database

Experiment & Result(3/9) 15 Handwriting and LM databases Corpora for LM training and dictionaries

Experiment & Result(4/9) 16 Validation PPL for N-gram LMs using different standard smoothing techniques.

Experiment & Result(5/9) 17 Validation PPL for different combinations of Witten– Bell smoothed N-gram LMs and NN LMs.

Experiment & Result(6/9) 18

Experiment & Result(7/9) 19

Experiment & Result(8/9) 20

Experiment & Result(9/9) 21

Conclusion 22 Two different recognition systems Based on recurrent neural networks Based on hybrid HMM/ANN models The hybrid HMM/ANN system is better to deal with large vocabularies than the BLSTM NN system. All the experimental data are over the best result published so far.

Thank you for listening. 23