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| <div class="section" id="connectionist-temporal-classification"> |
| <span id="connectionist-temporal-classification"></span><h1>Connectionist Temporal Classification<a class="headerlink" href="#connectionist-temporal-classification" title="Permalink to this headline">¶</a></h1> |
| <p><a class="reference external" href="https://www.cs.toronto.edu/~graves/icml_2006.pdf">Connectionist Temporal Classification</a> (CTC) is a cost function that is used to train Recurrent Neural Networks (RNNs) to label unsegmented input sequence data in supervised learning. For example, in a speech recognition application, using a typical cross-entropy loss, the input signal needs to be segmented into words or sub-words. However, using CTC-loss, it suffices to provide one label sequence for input sequence and the network learns both the alignment as well labeling. Baidu’s warp-ctc page contains a more detailed <a class="reference external" href="https://github.com/baidu-research/warp-ctc#introduction">introduction to CTC-loss</a>.</p> |
| <div class="section" id="ctc-loss-in-mxnet"> |
| <span id="ctc-loss-in-mxnet"></span><h2>CTC-loss in MXNet<a class="headerlink" href="#ctc-loss-in-mxnet" title="Permalink to this headline">¶</a></h2> |
| <p>MXNet supports two CTC-loss layers in Symbol API:</p> |
| <ul class="simple"> |
| <li><code class="docutils literal"><span class="pre">mxnet.symbol.contrib.ctc_loss</span></code> is implemented in MXNet and included as part of the standard package.</li> |
| <li><code class="docutils literal"><span class="pre">mxnet.symbol.WarpCTC</span></code> uses Baidu’s warp-ctc library and requires building warp-ctc library and mxnet library both from source.</li> |
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| <span id="lstm-ocr-example"></span><h2>LSTM OCR Example<a class="headerlink" href="#lstm-ocr-example" title="Permalink to this headline">¶</a></h2> |
| <p>MXNet’s example folder contains a <a class="reference external" href="https://github.com/apache/incubator-mxnet/tree/1.1.0/example/ctc">CTC example</a> for using CTC loss with an LSTM network to perform Optical Character Recognition (OCR) prediction on CAPTCHA images. The example demonstrates use of both CTC loss options, as well as inference after training using network symbol and parameter checkpoints.</p> |
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| <div class="section" id="next-steps"> |
| <span id="next-steps"></span><h2>Next Steps<a class="headerlink" href="#next-steps" title="Permalink to this headline">¶</a></h2> |
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| <h3><a href="../../index.html">Table Of Contents</a></h3> |
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| <li><a class="reference internal" href="#">Connectionist Temporal Classification</a><ul> |
| <li><a class="reference internal" href="#ctc-loss-in-mxnet">CTC-loss in MXNet</a></li> |
| <li><a class="reference internal" href="#lstm-ocr-example">LSTM OCR Example</a></li> |
| <li><a class="reference internal" href="#next-steps">Next Steps</a></li> |
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| Apache MXNet is an effort undergoing incubation at The Apache Software Foundation (ASF), <strong>sponsored by the <i>Apache Incubator</i></strong>. Incubation is required of all newly accepted projects until a further review indicates that the infrastructure, communications, and decision making process have stabilized in a manner consistent with other successful ASF projects. While incubation status is not necessarily a reflection of the completeness or stability of the code, it does indicate that the project has yet to be fully endorsed by the ASF. |
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