# Word separation in continuous sign language using isolated signs and post-processing

arXiv: https://arxiv.org/abs/2204.00923 · HTML: https://mlsanity.com/paper/2204.00923/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 36 references that are in the map.
- First posted on arXiv 2 Apr 2022.

## Builds on (most cited first, 6 of 6)

- [Fully Convolutional Networks for Continuous Sign Language Recognition](https://mlsanity.com/paper/2007.12402/) (2020)
- [Sign Language Production: A Review](https://mlsanity.com/paper/2103.15910/) (2021)
- [All You Need In Sign Language Production](https://mlsanity.com/paper/2201.01609/) (2022)
- [Interpretation of Swedish Sign Language using Convolutional Neural Networks and Transfer Learning](https://mlsanity.com/paper/2010.07827/) (2020)
- [ZS-SLR: Zero-Shot Sign Language Recognition from RGB-D Videos](https://mlsanity.com/paper/2108.10059/) (2021)
- [Multi-Modal Zero-Shot Sign Language Recognition](https://mlsanity.com/paper/2109.00796/) (2021)

## Built on by (most cited first, 3 of 3)

- [A Non-Anatomical Graph Structure for isolated hand gesture separation in continuous gesture sequences](https://mlsanity.com/paper/2207.07619/) (2022)
- [A Transformer Model for Boundary Detection in Continuous Sign Language](https://mlsanity.com/paper/2402.14720/) (2024)
- [A Transformer-Based Multi-Stream Approach for Isolated Iranian Sign Language Recognition](https://mlsanity.com/paper/2407.09544/) (2024)

## Abstract (arXiv)

. Continuous Sign Language Recognition (CSLR) is a long challenging task in Computer Vision due to the difficulties in detecting the explicit boundaries between the words in a sign sentence. To deal with this challenge, we propose a two-stage model. In the first stage, the predictor model, which includes a combination of CNN, SVD, and LSTM, is trained with the isolated signs. In the second stage, we apply a post-processing algorithm to the Softmax outputs obtained from the first part of the model in order to separate the isolated signs in the continuous signs. While the proposed model is trained on the isolated sign classes with similar frame numbers, it is evaluated on the continuous sign videos with a different frame length per each isolated sign class. Due to the lack of a large dataset, including both the sign sequences and the corresponding isolated signs, two public datasets in Isolated Sign Language Recognition (ISLR), RKS-PERSIANSIGN and ASLLVD, are used for evaluation. Results of the continuous sign videos confirm the efficiency of the proposed model to deal with isolated sign boundaries detection.