Abstract
Sign language for communication is efficacious for humans, and vital research is in progress in computer vision systems. The earliest work in Indian Sign Language (ISL) recognition considers the recognition of significant differentiable hand signs and therefore often selecting a few signs from the ISL for recognition. This paper deals with robust modeling of static signs in the context of sign language recognition using deep learning-based convolutional neural networks (CNN). In this research, total 35,000 sign images of 100 static signs are collected from different users. The efficiency of the proposed system is evaluated on approximately 50 CNN models. The results are also evaluated on the basis of different optimizers, and it has been observed that the proposed approach has achieved the highest training accuracy of 99.72% and 99.90% on colored and grayscale images, respectively. The performance of the proposed system has also been evaluated on the basis of precision, recall and F-score. The system also demonstrates its effectiveness over the earlier works in which only a few hand signs are considered for recognition.
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Acknowledgements
This publication is an outcome of R&D work undertaken in the project under the Visvesvaraya PhD Scheme of Ministry of Electronics and Information Technology, Government of India, being implemented by Digital India Corporation (formerly Media Lab Asia). We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan XP GPU used for this research.
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Appendix
Appendix
Grayscale sign samples showing precision, recall and F1 score
S no. | Sign | Precision | Recall | F1-score | S no. | Sign | Precision | Recall | F1-score |
|---|---|---|---|---|---|---|---|---|---|
1 | A | 1.00 | 0.96 | 0.98 | 51 | Leprosy | 1.00 | 1.00 | 1.00 |
2 | Afraid | 0.97 | 0.97 | 0.97 | 52 | M | 0.97 | 0.79 | 0.87 |
3 | Add | 1.00 | 1.00 | 1.00 | 53 | Me | 1.00 | 1.00 | 1.00 |
4 | B | 1.00 | 1.00 | 1.00 | 54 | N | 1.00 | 1.00 | 1.00 |
5 | Bottle | 1.00 | 0.97 | 0.99 | 55 | Nose | 0.98 | 1.00 | 0.99 |
6 | Bud | 1.00 | 1.00 | 1.00 | 56 | Nine | 1.00 | 1.00 | 1.00 |
7 | Bent | 0.97 | 1.00 | 0.99 | 57 | Nurse | 0.99 | 0.98 | 0.98 |
8 | Between | 1.00 | 0.99 | 0.99 | 58 | O | 0.96 | 0.97 | 0.96 |
9 | Blind | 1.00 | 1.00 | 1.00 | 59 | Oath | 1.00 | 1.00 | 1.00 |
10 | Bowl | 0.97 | 1.00 | 0.98 | 60 | One | 1.00 | 0.99 | 0.99 |
11 | Brain | 0.99 | 0.99 | 0.99 | 61 | Open | 1.00 | 0.97 | 0.98 |
12 | C | 1.00 | 1.00 | 1.00 | 62 | Owl | 1.00 | 1.00 | 1.00 |
13 | Coolie | 0.97 | 0.94 | 0.96 | 63 | P | 1.00 | 0.97 | 0.98 |
14 | Cough | 1.00 | 1.00 | 1.00 | 64 | Policy | 0.98 | 1.00 | 0.99 |
15 | Cow | 0.99 | 0.97 | 0.98 | 65 | Pray | 1.00 | 1.00 | 1.00 |
16 | Chest | 1.00 | 1.00 | 1.00 | 66 | Promise | 1.00 | 1.00 | 1.00 |
17 | Claw | 1.00 | 1.00 | 1.00 | 67 | Q | 0.97 | 1.00 | 0.99 |
18 | D | 0.79 | 0.97 | 0.87 | 68 | R | 0.98 | 1.00 | 0.99 |
19 | Devil | 0.98 | 0.94 | 0.96 | 69 | S | 0.95 | 1.00 | 0.97 |
20 | Doctor | 0.98 | 1.00 | 0.99 | 70 | Seven | 0.96 | 1.00 | 0.98 |
21 | E | 1.00 | 1.00 | 1.00 | 71 | Soldier | 1.00 | 1.00 | 1.00 |
22 | East | 1.00 | 1.00 | 1.00 | 72 | Shirt | 1.00 | 1.00 | 1.00 |
23 | Eight | 0.96 | 0.90 | 0.93 | 73 | Six | 1.00 | 0.97 | 0.99 |
24 | Evening | 1.00 | 0.96 | 0.98 | 74 | Sick | 1.00 | 1.00 | 1.00 |
25 | Elbow | 0.95 | 1.00 | 0.98 | 75 | Skin | 1.00 | 1.00 | 1.00 |
26 | Eye | 1.00 | 1.00 | 1.00 | 76 | Shoulder | 1.00 | 0.98 | 0.99 |
27 | F | 0.97 | 0.97 | 0.97 | 77 | Stand | 1.00 | 1.00 | 1.00 |
28 | Fat | 1.00 | 1.00 | 1.00 | 78 | Strong | 0.97 | 1.00 | 0.98 |
29 | Faith | 0.98 | 1.00 | 0.99 | 79 | Sleep | 1.00 | 0.95 | 0.98 |
30 | Fever | 0.95 | 1.00 | 0.97 | 80 | Sunday | 1.00 | 1.00 | 1.00 |
31 | Feel | 0.97 | 1.00 | 0.98 | 81 | T | 0.99 | 1.00 | 0.99 |
32 | Few | 1.00 | 1.00 | 1.00 | 82 | Ten | 1.00 | 0.98 | 0.99 |
33 | Food | 0.98 | 0.86 | 0.92 | 83 | Telephone | 1.00 | 1.00 | 1.00 |
34 | Four | 1.00 | 0.96 | 0.98 | 84 | Tongue | 0.99 | 1.00 | 0.99 |
35 | Fist | 0.97 | 0.98 | 0.97 | 85 | Thorn | 0.96 | 1.00 | 0.98 |
36 | Five | 1.00 | 1.00 | 1.00 | 86 | Three | 0.97 | 1.00 | 0.99 |
37 | G | 0.97 | 0.97 | 0.97 | 87 | Trouble | 1.00 | 0.95 | 0.97 |
38 | Gun | 0.97 | 1.00 | 0.99 | 88 | Two | 1.00 | 1.00 | 1.00 |
39 | Good | 1.00 | 1.00 | 1.00 | 89 | U | 1.00 | 0.99 | 0.99 |
40 | H | 1.00 | 1.00 | 1.00 | 90 | V | 1.00 | 1.00 | 1.00 |
41 | Hair | 1.00 | 1.00 | 1.00 | 91 | W | 1.00 | 1.00 | 1.00 |
42 | Hand | 0.97 | 1.00 | 0.98 | 92 | West | 1.00 | 0.93 | 0.96 |
43 | Head | 0.90 | 0.99 | 0.94 | 93 | Wedding | 0.99 | 0.99 | 0.99 |
44 | Hear | 0.97 | 1.00 | 0.98 | 94 | Water | 0.93 | 0.98 | 0.95 |
45 | I | 1.00 | 1.00 | 1.00 | 95 | White | 1.00 | 0.98 | 0.99 |
46 | Jain | 0.99 | 1.00 | 0.99 | 96 | X | 1.00 | 1.00 | 1.00 |
47 | K | 0.98 | 0.98 | 0.98 | 97 | Y | 1.00 | 1.00 | 1.00 |
48 | King | 1.00 | 0.95 | 0.97 | 98 | You | 0.97 | 1.00 | 0.99 |
49 | L | 1.00 | 1.00 | 1.00 | 99 | Z | 0.99 | 1.00 | 0.99 |
50 | Love | 1.00 | 0.98 | 0.99 | 100 | Zero | 1.00 | 1.00 | 1.00 |
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Wadhawan, A., Kumar, P. Deep learning-based sign language recognition system for static signs. Neural Comput & Applic 32, 7957–7968 (2020). https://doi.org/10.1007/s00521-019-04691-y
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DOI: https://doi.org/10.1007/s00521-019-04691-y