* Add attention training model for ocr.

* Add beam search for infer.

* Fix data reader.

* Fix inference.

* Prune result of inference.

* Fix README

* update README

* Rsize figure.

* Resize image and fix format.

* Add MKL-DNN Benchmarking to CRNN-CTC

* Make crnn-ctc scripts more portable

* Add documentation for cycle to crnn-ctc-reader

* Update crnn-ctc readme for CPU execution

* Merge CRNN-CTC train & inference scripts

* Fix mnist model & ce, kaffe graph yapf issues

* Remove LD_LIBRARY_PATH from crnn-ctc scripts

* CRNN-CTC scripts: set parallel to true

Abort script if batch_size is lower than num of cores

* CRNN-CTC scripts: limit mode options in infer

* CRNN-CTC scripts: set mkldnn parallel to False

* Refine code for English dataset.
1. Remove a pooling layer.
2. Change classes_num to 94.
3. Modify some arguments in ctc_train.py
4. Add learning rate decay policy.

* Fix readme.

* Fix README.

* Remove consine decay.

* Remove eval.sh

1. Remove unused  arguments.
2. Refine doc.
3. Change 'device' to 'use_gpu'.

1. Remove illustration of arguments.
2. Make inference support for more format input.

1. Add document.
2. Add arguments for saving model and init model.
3. Refine inference.py and eval.py.
4. Make ctc_reader.py support for custom data.

1. Move all network defining to 'crnn_ctc_model.py'
2. Add initilizer for some layers
3. Rename 'fluid/ocr' to 'fluid/ocr_recognition'
4. Remove copyright
5. Rename some functions

1. Remove 'ocr_ctc' directory to 'ocr'.
2. Init README.md
3. Fix learning rate and l2
4. Refine training log format
5. Reduce arguments of train function
6. Set filter_size of im2sequence dynamicly
7. Add fc op before GRU op