Using this decoder, words are constrained to those contained in a dictionary, but arbitrary non-word character strings (numbers, punctuation marks) can still be recognized. Integrate word beam search decodingīesides the two decoders shipped with TF, it is possible to use word beam search decoding. Two examples: if you want to infer using beam search, execute python main.py -beamsearch, while you have to execute python main.py -train -beamsearch if you want to train the NN and do the validation using beam search. If neither -train nor -validate is specified, the NN infers the text from the test image ( data/test.png). It should not be used when training the NN. This is a custom TF operation and must be compiled from source, more information see corresponding section below.
-wordbeamsearch: use word beam search decoding (only outputs words contained in a dictionary) instead of best path decoding. -beamsearch: use vanilla beam search decoding (better, but slower) instead of best path decoding. -validate: validate the NN, details see below. -train: train the NN, details see below. Validation character error rate of saved model: 10.624916% The input image and the expected output is shown below. Afterwards, go to the src/ directory and run python main.py. Take care that the unzipped files are placed directly into the model/ directory and not some subdirectory created by the unzip-program. Go to the model/ directory and unzip the file model.zip (pre-trained on the IAM dataset). to recognize text-lines) or want better recognition accuracy. I will give some hints how to extend the model in case you need larger input-images (e.g. 3/4 of the words from the validation-set are correctly recognized and the character error rate is around 10%. As these word-images are smaller than images of complete text-lines, the NN can be kept small and training on the CPU is feasible. This Neural Network (NN) model recognizes the text contained in the images of segmented words as shown in the illustration below. Handwritten Text Recognition (HTR) system implemented with TensorFlow (TF) and trained on the IAM off-line HTR dataset. Handwritten Text Recognition (HTR) system implemented with TensorFlow. Node : This Project on Github and Open Source Project Handwritten Text Recognition with TensorFlow
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