Not Optimized for GPU and batch processing.However, using CRAFT /EAST for Text Detection(Deep learning models and supports multilingual text detection) and then using Tesseract for Text Recognition will yield better results in STR. Since it uses the classical page layout analysis technique, text detection is not as accurate compared to its deep learning peers and cannot be directly used for scene text detection.Multilingual and other configuration options.Still a better open-source option for scanned documents.Optimized for CPU, and has wrappers in multiple programming languages.Blacklisting: -c tessedit_char_blacklist=.Whitelisting: -c tessedit_char_whitelist=.Tesseract allows us to configure the output text using the “-c” option. Tesseract 4 introduced LSTM models for Text recognition which often works best, still, you can use the Tesseract 3 Legacy mode or Combine Legacy + LSTM using the OEM option 0 Legacy engine only. We can add languages separated by the “+” sign, -l deu+eng.Using the “-l” option we can use/add languages supported by tesseract.tessdata_best: Best trained models of tesseract OCR and acts as the base models for fine-tuning.There will be some speed-accuracy trade-offs but worthy to experiment. tessdata_fast: Tesseract is written in C++ and optimized for performance but if you further need to improve the performance, try using tessdata_fast models which are 8-bit integer versions of the tessdata models.Tesseract uses Leptonica for pre-processing and text segmentation and has various options for page segmentation. Tesseract is vast, so experimenting with various options can improve the performance substantially. One Size Doesn’t Fit All Configuring Tesseract: Tesseract is one of the most popular OCR open-source engines developed in C++ and has wrappers available for Python, Java, Swift, Ruby, etc, and recognizes text from more than 100 languages. Check out this blog if you’re interested to know about various pre-processing options to improve the OCR quality. We’ll evaluate the tools for both of these scenarios.Īny OCR output is as good as the input document, so understanding the input document and accordingly designing pre-processing pipelines will give a performance boost irrespective of what OCR engine you use. Digital Images: Typed / Handwritten text, considered as Scene Text Recognition or OCR in the Wildīoth of these areas have their own challenges and require different components(For example, pre-processing pipeline) in various stages of the OCR pipeline.Scanned Documents: Printed / Handwritten text recognition, commonly considered as OCR problem.While we have control over the Output(it can be plain text, hOCR, XML, Editable PDF), the nature of the input varies based on the use case and needs to be considered before building any OCR pipeline. Optical Character Recognition converts Images/Scanned Documents(Input) into editable and searchable machine-encoded text(Output).
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