Hi All

I am training Tesseract for Perso-Arabic languages using my custom dataset.
I get *Segmentation fault-core dumped* error and training gets stopped.
Net-spec used: net_spec '[1,0,0,1 Ct5,5,16 Ct3,3,8 Mp3,3 Lfys64 Lfx128
Lbx64 Lby64 Lrx128 Lfx128 O1c105]'.Attaching the terminal error output
below:


Thread 1 "lstmtraining" received signal SIGSEGV, Segmentation fault.
tesseract::Logistic (x=-nan(0x8000000000000)) at functions.h:90
90  double l0 = LogisticTable[index];
(gdb) bt
#0  tesseract::Logistic (x=-nan(0x8000000000000)) at functions.h:90
#1  tesseract::FFunc::operator() (this=<synthetic pointer>,
    x=-nan(0x8000000000000)) at functions.h:98
#2  tesseract::FuncInplace<tesseract::FFunc> (inout=<optimized out>,
    n=<optimized out>) at functions.h:163
#3  tesseract::LSTM::Forward () at lstm.cpp:364
#4  0x00007ffff625d30c in GOMP_parallel_sections ()
   from /usr/lib/x86_64-linux-gnu/libgomp.so.1
#5  0x00007ffff7a3ccd6 in tesseract::LSTM::Forward (this=0x55555f713cd0,
    debug=<optimized out>, input=..., input_transpose=<optimized out>,
    scratch=0x7fffffffbf58, output=0x555562b56c00) at lstm.cpp:322
#6  0x00007ffff7a543eb in tesseract::Parallel::Forward
(this=0x555556504490,
    debug=false, input=..., input_transpose=<optimized out>,
    scratch=0x7fffffffbf58, output=0x555562b61460) at parallel.cpp:93
#7  0x00007ffff7a60d01 in tesseract::Reversed::Forward
(this=0x55556030af00,
    debug=<optimized out>, input=..., input_transpose=<optimized out>,
    scratch=0x7fffffffbf58, output=0x55555f715060) at reversed.cpp:60
#8  0x00007ffff7a6293f in tesseract::Series::Forward (this=0x555560454110,
    debug=<optimized out>, input=..., input_transpose=<optimized out>,
    scratch=0x7fffffffbf58, output=0x7fffffffad80) at series.cpp:119
#9  0x00007ffff7a410ab in tesseract::LSTMRecognizer::RecognizeLine (
    this=this@entry=0x7fffffffbc18, image_data=..., invert=<optimized out>,
    debug=debug@entry=false, re_invert=<optimized out>,
---Type <return> to continue, or q <return> to quit---


Any help to solve this shall be highly appreciated.

Thanks
Shubham

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