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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Bluche, T. Ney, H. Kermorvant, C. |
| Copyright Year | 2015 |
| Description | Author affiliation: Spoken Language Process. Group, LIMSI, Orsay, France (Bluche, T.; Ney, H.) || A2iA SA, Paris, France (Kermorvant, C.) |
| Abstract | In this paper we present the handwriting recognition systems submitted by the LIMSI to the HTRtS 2014 contest. The systems for both the restricted and unrestricted tracks consisted of combination of several optical models. We extracted handcrafted features as well as pixels values with a sliding window. We trained Deep Neural Networks (DNNs) and Bidirectional Long Short-Term Memory Recurrent Neural Networks (BLSTM-RNNs), which where plugged as the optical model in Hidden Markov Models (HMMs). We propose a novel method to build language models that can cope with hyphenation in the text. The combination was performed from lattices generated from the different systems. We were the only team participating in both tracks and ranked second in each. The final Word Error Rates were 15.0% and 11.0% for the restricted (resp. unrestricted) track. We studied the impact of adding data for optical and language modeling. After the evaluation, we also used the same corpus for the language model as the winning team and obtained comparable results. |
| Starting Page | 86 |
| Ending Page | 90 |
| File Size | 316097 |
| Page Count | 5 |
| File Format | |
| e-ISBN | 9781479918058 |
| DOI | 10.1109/ICDAR.2015.7333731 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2015-08-23 |
| Publisher Place | Tunisia |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Hidden Markov models Lattices RNA Analytical models |
| Content Type | Text |
| Resource Type | Article |
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