If you'd like to run these models in your own ExecuTorch runtime, refer to the
official documentation
for setup instructions.
Each language ships as
one fused
.pte
(CRAFT
detect
+ CRNN
recognize
in a single
file) per backend, using
static bucketed methods
— no dynamic-shape footguns. The
.pte
is a pure tensor→tensor function; all pre/post-processing (resize, normalize, box extraction,
crop, CTC decode) is the client's job and is driven by
config.json
.
Languages
code
charset size
code
charset size
english
96
korean
1008
latin
351
telugu
165
japanese
2214
kannada
167
zh_sim
6718
cyrillic
207
All languages share the same CRAFT detector and CRNN architecture — they differ
only
in
the recognizer charset. The detector half of each fused PTE is identical across languages.
config.json
is
shared
across all languages; it carries
charsetUrlPattern: "easyocr_<lang>.charset.txt"
so the client resolves the charset by
language. Charset index
i
maps to logit
i + 1
(logit
0
is the CTC blank).
recognize
(widths, height 64):
64, 128, 256, 512
→
recognize_64 … recognize_512
detect
runs once per image;
recognize
runs once per text line.
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-easy-ocr on huggingface.co
548
Total runs
0
24-hour runs
-67
3-day runs
-146
7-day runs
72
30-day runs
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