keeganyip/EasyOCRST

EasyOCR weights (CRAFT detector and English recogniser) in safetensors

The two pretrained models the EasyOCR en reader uses, converted from the official .pth files to .safetensors. Nothing was retrained or modified: every tensor is identical, in value and dtype, to the original, and the key names (including the module. prefix) are unchanged.

File	Model	Tensors	SHA-256
craft_mlt_25k.safetensors	CRAFT text detector	154	4ab7f69bd8161b437f18f1718b8f6e58136bd081639f238b567e987415c20268
english_g2.safetensors	English recogniser (CRNN, "generation 2")	44	307a47690148280d00e4314b1868ebce44089d0974903a56fe7822902d1b31d0

SHA256SUMS lists the same values: sha256sum -c SHA256SUMS.

Origin

Converted from the official EasyOCR release assets (EasyOCR's own MD5 values in easyocr/config.py are shown):

Original	Release asset	MD5 of the original .pth
craft_mlt_25k.pth	https://github.com/JaidedAI/EasyOCR/releases/download/pre-v1.1.6/craft_mlt_25k.zip	2f8227d2def4037cdb3b34389dcf9ec1
english_g2.pth	https://github.com/JaidedAI/EasyOCR/releases/download/v1.3/english_g2.zip	5864788e1821be9e454ec108d61b887d

Conversion: torch.load(..., weights_only=True) of the state dict, then safetensors.torch.save_file. The saved file was loaded back and compared tensor by tensor with the original (all equal). An EasyOCR reader fed these tensors returned the same boxes, text and confidences as the original reader on the test images it was checked with (CPU).

Use

These are state dicts, not a ready easyocr.Reader folder: EasyOCR itself only looks for MD5-checked .pth files, so the detector and recogniser have to be built from the state dicts by the caller.

from safetensors.torch import load_file

craft = load_file("craft_mlt_25k.safetensors")   # CRAFT state dict (keys start with "module.")
recog = load_file("english_g2.safetensors")      # recogniser state dict (keys start with "module.")

Licence and attribution

The files are redistributed under the licences of the projects they come from. Keep these notices with them.

• EasyOCR, JaidedAI: Apache License 2.0. https://github.com/JaidedAI/EasyOCR
• CRAFT (craft_mlt_25k), Clova AI Research, NAVER Corp.: MIT License. https://github.com/clovaai/CRAFT-pytorch. Paper: Baek et al., "Character Region Awareness for Text Detection", CVPR 2019 (https://arxiv.org/abs/1904.01941). EasyOCR ships Clova AI's pretrained CRAFT model.
• Recognition training pipeline, a modified version of clovaai/deep-text-recognition-benchmark: Apache License 2.0. https://github.com/clovaai/deep-text-recognition-benchmark. Model architecture: CRNN (Shi et al., 2015).

The upstream projects publish licences for their code. The weight files carry no separate licence statement that was found, so they are assumed to follow the repositories above. This repository is an unofficial conversion and is not affiliated with JaidedAI or Clova AI.