# Experimenting the Automatic Recognition of Non-Conventionalized Units in Sign Language
## 4 Conclusions  
In this work, we have first focused on improving the input data for CSLR systems, proposing Dicta-Sign-LSF-v2, a LSF corpus previously made by linguists. We then developed a general description of the problem of CSLR with adapted metrics. In order to realize a first series of experiments with this broader definition of CSLR, we have introduced and implemented an original combination of signer representation and learning model, using a mix of publicly available and self-developed models and a convolutional and recurrent neural network.  
Finally, we have conducted a thorough analysis of the recognition performance of the proposed model for four very different linguistic descriptors--FLSs, DSs, PTSs and FBuoys--on Dicta-Sign-LSF-v2. We have shown that promising performance values are met. A qualitative analysis on the test set then illustrates the merits of the proposed approach.  
As regards perspectives of this work, signer representation--in particular with respect to hand modeling--shows a lot of room for improvement. Independently, gathering quality annotated SL data is a major challenge, as the amount of training data appears to be a bottleneck for the performance of CSLR models.  
Conceptualization, V.B., A.B. and M.G.; methodology, V.B., A.B. and M.G.; software, V.B.; validation, V.B., A.B. and M.G.; formal analysis, V.B.; investigation, V.B., A.B. and M.G.; resources, A.B. and M.G.; data curation, V.B. and A.B.; writing--original draft preparation, V.B.; writing--review and editing, V.B., A.B. and M.G.; visualization, V.B., A.B. and M.G. supervision, A.B. and M.G.; project administration, A.B. and M.G.; funding acquisition, A.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
Machine Learning and Image Processing  
BLSTM Bidirectional LSTM  
CNN Convolutional Neural Network  
CRF Conditional Random Field  
CTC Connectionist Temporal Classification  
DNN Deep Neural Network  
DTW Dynamic Time Warping  
EM Expectation Maximization  
FC Fully Connected  
fps frames per second  
HMM Hidden Markov Model  
HOG Histogram of Oriented Gradients  
LSTM Long Short-Term Memory  
NMT Neural Machine Translation  
OP OpenPose [47]  
RCNN Recurrent Convolutional Neural Network  
RGB Red-Green-Blue  
RNN Recurrent Neural Network  
SVM Support Vector Machine  
WER Word Error RateSign Language  
ASL American Sign Language  
ChSL Chinese Sign Language  
CLexSR Continuous Lexical Sign Recognition  
CSL Continuous Sign Language  
CSLR Continuous Sign Language RecognitionAlgorithms  
DGS German Sign Language (_Deutsche Gehrdensprache_)  
DTS Danish Sign Language (_Dansk Tegnsprog_)  
GR Gesture Recognition  
HS Hand shape  
LSF French Sign Language (_Lengue des Signes Francaise_)  
NZSL New Zealand Sign Language  
SD Signer-Dependent (_see_ signer-dependent)  
SGI Highly Iconic Structure (_Structure de Grande Iconicite_) [68]  
SI Signer-Independent (_see_ signer-independent)  
SL Sign Language  
SLR Sign Language Recognition  
SLT Sign Language Translation  
SLU Sign Language Understanding  
TD Task Dependent  
TI Task Independent  
T-S Situational Transfer  
T-P Transfer of Persons  
T-FS Transfer of Form and Size  
Sign Language annotation categories  
FLS Fully Lexical Sign  
PLS Partially Lexical Sign  
DS Depicting Sign  
DS-L DS-Location (of an entity)  
DS-Motion (of an entity)  
DS-SS DS-Size&Shape (of an entity)  
DS-G DS-Ground (spatial or temporal reference)  
PTS Pointing Sign  
FBuoy Fragment Buoy  
NLS Non Lexical Sign  
FS Fingerspelled Sign  
NS Numbering Sign  
G Gesture