If you'd like to run these models in your own ExecuTorch runtime, refer to the
official documentation
for setup instructions.
PP-OCRv6 is the
primary
OCR pipeline — smallest and fastest. It ships as
one fused
.pte
per backend, using
static bucketed
methods
. The
.pte
is a pure tensor→tensor function; all pre/post-processing (resize,
normalize, DBNet box decode, perspective crop, CTC decode) is the client's job and is driven by
config.json
. One model covers
all languages
(18 709-entry multilingual charset).
Backends
backend
target
detect
recognize
xnnpack
CPU
int8 (DBNet)
fp32 (SVTR)
coreml
Apple ANE
weight-only int8
weight-only int8
vulkan
Android GPU
fp16 (GPU)
fp32 on
XNNPACK
(mixed-delegate)
Vulkan is mixed-delegate
: DBNet detects on the GPU, but the SVTR recognizer runs on the
CPU (XNNPACK). int8 SVTR is lossy on the 18 709-token vocab — so the recognizer stays fp32 on CPU for correctness.
Detector input is RGB, normalized
(x/255 − 0.5)/0.5
.
Compatibility
If you intend to use these models outside of React Native ExecuTorch, make sure your runtime is
compatible with the
ExecuTorch
version used to export the
.pte
files. For more details, see
the compatibility note in the
ExecuTorch GitHub repository
.
If you work with React Native ExecuTorch, the library constants guarantee compatibility with the
runtime used behind the scenes.
Runs of software-mansion react-native-executorch-pp-ocrv6 on huggingface.co
208
Total runs
-22
24-hour runs
-34
3-day runs
44
7-day runs
77
30-day runs
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