Has anyone used a RPI4 for OCR (Tesseract)? How did you feel about execution speed?

• tessdata

  10 5,934 2.8

  Trained models with fast variant of the "best" LSTM models + legacy models

  

Then you could try different engines. --oem 0 disables the shiny new neural network stuff and uses the classic Tesseract engine; --oem 1 does the opposite. Try both, see which works best in terms of performance and accuracy for your particular use-case. You'll need to have training data for the legacy engine, though. These would work, or you could try tessdata_fast, which is specifically built for speed.

• tessdata_fast

  1 442 4.6

  Fast integer versions of trained LSTM models

  

Then you could try different engines. --oem 0 disables the shiny new neural network stuff and uses the classic Tesseract engine; --oem 1 does the opposite. Try both, see which works best in terms of performance and accuracy for your particular use-case. You'll need to have training data for the legacy engine, though. These would work, or you could try tessdata_fast, which is specifically built for speed.

• InfluxDB

  www.influxdata.com featured

  Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

  

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