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Python-based OCR package using recurrent neural networks.
This project forked from ocropus-archive/dup-ocropy
Python-based OCR package using recurrent neural networks.
To install, use: $ sudo apt-get install $(cat PACKAGES) $ python setup.py download_models $ sudo python setup.py install To test the recognizer, run: $ ./run-test OCRopus is really a collection of document analysis programs, not a turn-key OCR system. In addition to the recognition scripts themselves, there are a number of scripts for ground truth editing and correction, measuring error rates, determining confusion matrices, etc. OCRopus commands will generally print a stack trace along with an error message; this is not generally indicative of a problem (in a future release, we'll suppress the stack trace by default since it seems to confuse too many users). To recognize pages of text, you need to run separate commands: binarization, page layout analysis, and text line recognition. # perform binarization ./ocoropus-nlbin tests/ersch.png -o book # perform page layout analysis ./ocropus-gpageseg 'book/????.bin.png' # perform text line recognition (on four cores, with a fraktur model) ./ocropus-rpred -Q 4 -m models/fraktur.pyrnn.gz 'book/????/??????.bin.png' # generate HTML output ./ocropus-hocr 'book/????.bin.png' -o ersch.html # display the output firefox ersch.html There are also a number of older commands for text line recognition, layout analysis, etc., kept for backwards compatibility. The binarization and layout analysis commands of the current release will be replaced in the next release with entirely new, trainable commands. The main feature of this release is ocropus-rpred, which achieves very low error rates on a wide variety of fonts and inputs (even degraded) of body text, even without language models or dictionaries. The model has been trained on UW3 and UNLV data. Test set error on UW3 is about 0.5% without a language model or dictionary. There are some things the currently trained models for ocropus-rpred will not handle well, largely because they are nearly absent in the current training data. That includes all-caps text, some special symbols (including "?"), typewriter fonts, and subscripts/superscripts. This will be addressed in a future release, and, of course, you are welcome to contribute new, trained models.
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