kirandevraj/ISL-Fingerspelling

ISL Fingerspelling — model weights

Checkpoints for the ICPR 2026 paper Dense Frame Annotations for Low-Resource ISL Fingerspelling Recognition (Springer).

Inference code: https://github.com/Kirandevraj/ISL-Fingerspelling-Dense Dataset: https://huggingface.co/datasets/kirandevraj/ISL-Fingerspelling

These files are downloaded automatically on first run — you do not need to fetch them by hand:

git clone https://github.com/Kirandevraj/ISL-Fingerspelling-Dense
cd ISL-Fingerspelling-Dense
pip install -r requirements.txt
bash demo.sh

Files

File	Size	What it is
recognition_standard.pt	57.5 MB	ResNet-18 + 2-layer BiLSTM + CTC head, trained with CTC and frame-level cross-entropy. Standard split.
recognition_signer.pt	57.5 MB	Same architecture, signer-independent split.
frame_classifier_standard.pt	44.8 MB	Stage-1 ResNet-18 frame classifier, 27 classes. Drives localization.
frame_classifier_signer.pt	44.8 MB	Same, signer-independent split.

Recognition is 14.3M parameters. Input is 224×224 RGB frames from signer-cropped video; the CTC vocabulary is 28 (blank, space, a–z), while the standalone frame classifier has 27 outputs (space, a–z — no blank).

Citation

@inproceedings{islfs2026,
  title     = {Dense Frame Annotations for Low-Resource ISL Fingerspelling Recognition},
  booktitle = {International Conference on Pattern Recognition (ICPR)},
  year      = {2026},
  doi       = {10.1007/978-3-032-31930-2_18}
}