See testing section in DAS Tutorial 2016 for accuracy rates for different languages.
Big test in Google Data Center (Hindi?)
| Engine | Total char errors | Word Recall Errors | Word Precision Errors | Walltime | CPUtime* |
|---|---|---|---|---|---|
| Tess 3.04 | 13.9 | 30 | 31.2 | 3.0 | 2.8 |
| Cube | 15.1 | 29.5 | 30.7 | 3.4 | 3.1 |
| Tess+Cube | 11.0 | 24.2 | 25.4 | 5.7 | 5.3 |
| LSTM | 7.6 | 20.9 | 20.8 | 1.5 | 2.5 |
Note in the above table that LSTM is faster than Tess 3.04 (without adding cube) in both wall time and CPU time! For wall time by a factor of 2.
Median of three results from test on HP Z420 on a single Hindi page.
| Test Mode | Real | User |
|---|---|---|
| Original (cube + tess) | 7.6 | 7.3 |
| Base Tess | 2.9 | 2.6 |
| Cube | 5.4 | 4.9 |
| LSTM With OpenMP+AVX | 1.8 | 3.8 |
| LSTM No OpenMP with AVX | 2.7 | 2.4 |
| LSTM No OpenMP with SSE | 3.1 | 2.7 |
| LSTM No OpenMP no SIMD at all | 4.6 | 4.1 |
My first test with a simple screenshot gave significant better results with LSTM, but needed 16 minutes CPU time (instead of 9 seconds) with a debug build of Tesseract (
-O0). A release build (-O2) needs 17 seconds with LSTM, 4 seconds without for the same image.
The slow speed with debug is to be expected. The new code is much more memory intensive, so it is a lot slower on debug (also OpenMP is turned off by choice on debug). The optimized build speed sounds about right for Latin-based languages. It is the complex scripts that will run faster relative to base Tesseract.
Ref: https://github.com/tesseract-ocr/tesseract/issues/40