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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
This repository lets you train neural networks models for performing end-to-end full-page handwriting recognition using the Apache MXNet deep learning frameworks on the IAM Dataset.
| Date | Stars |
|---|---|
| 2026-07-31 | 526 |
| 2026-08-01 | 526 |
| 2026-08-02 | 526 |
| 2026-08-06 | 526 |
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# Handwritten Text Recognition (OCR) with MXNet Gluon ## Local Setup `git clone https://github.com/awslabs/handwritten-text-recognition-for-apache-mxnet --recursive` You need to install SCLITE for WER evaluation You can follow the following bash script from this folder: ```bash cd .. git clone https://github.com/usnistgov/SCTK cd SCTK export CXXFLAGS="-std=c++11" && make config make all make check make install make doc cd - ``` You also need hsnwlib ```bash pip install pybind11 numpy setuptools cd .. git clone https://github.com/nmslib/hnswlib cd hnswlib/python_bindings python setup.py install cd ../.. ``` if "AssertionError: Please enter credentials for the IAM dataset in credentials.json or as arguments" occurs rename credentials.json.example and to credentials.json with your username and password. ## Overview  The pipeline is composed of 3 steps: - Detecting the handwritten area in a form [[blog post](https://medium.com/apache-mxnet/page-segmentation-with-gluon-dcb4e5955e2)], [[jupyter notebook](https://github.com/awslabs/handwritten-text-recognition-for-apache-mxnet/blob/master/1_b_paragraph_segmentation_dcnn.ipynb)], [[python script](https://github.com/awslabs/handwritten-text-recognition-for-apache-mxnet/blob/master/ocr/scripts/paragraph_segmentation_dcnn.py)] - Detecting lines of handwritten texts [[blog post](https://medium.com/apache-mxnet/handwriting-ocr-line-segmentation-with-gluon-7af419f3a3d8)], [[jupyter notebook](https://github.com/awslabs/handwritten-text-recognition-for-apache-mxnet/blob/master/2_line_word_segmentation.ipynb)], [[python script](https://github.com/awslabs/handwritten-text-recognition-for-apache-mxnet/blob/master/word_and_line_segmentation.py)] - Recognising characters and applying a language model to correct errors. [[blog post](https://medium.com/apache-mxnet/handwriting-ocr-handwriting-recognition-and-language-modeling-with-mxnet-gluon-4c7165788c67)], [[jupyter notebook](https://github.com/awslabs/handwritten-text-recognition-for-apache-mxnet/blob/master/3_handwriting_recognition.ipynb)], [[python script](https://github.com/awslabs/handwritten-text-recognition-for-apache-mxnet/blob/master/ocr/scripts/handwriting_line_recognition.py)] The entire inference pipeline can be found in this [notebook](https://github.com/awslabs/handwritten-text-recognition-for-apache-mxnet/blob/master/0_handwriting_ocr.ipynb). See the *pretrained models* section for the pretrained models. A recorded talk detailing the approach is available on youtube. [[video](https://www.youtube.com/watch?v=xDcOdif4lj0)] The corresponding slides are available on slideshare. [[slides](https://www.slideshare.net/apachemxnet/ocr-with-mxnet-gluon)] ## Pretrained models: You can get the models by running `python get_models.py`: ## Sample results  The greedy, lexicon search, and beam search outputs present similar and reasonable predictions for the selected examples. In Figure 6, interesting examples are presented. The first line of Figure 6 show cases where the lexicon search algorithm provided fixes that corrected the words. In the top example, “tovely” (as it was written) was corrected “lovely” and “woved” was corrected to “waved”. In addition, the beam search output corrected “a” into “all”, however it missed a space between “lovely” and “things”. In the second example, “selt” was converted to “salt” with the lexicon search output. However, “selt” was erroneously converted to “self” in the beam search output. Therefore, in this example, beam search performed worse. In the third example, none of the three methods significantly provided comprehensible results. Finally, in the forth example, the lexicon search algorithm incorrectly converted “forhim” into “forum”, however the beam search algorithm correctly identified “for him”. ## Dataset: * To use test_iam_dataset.ipynb,
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Thomas Delteil · AWS AI · Canada
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Ehsan M. Kermani · @modular · Canada
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:5e9bab4df8c1fd14, desc:dataset