Converting sign language gestures from digital images to text

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Converting sign language gestures from digital images to text
ASL2TXT Converting sign language gestures from digital images to text George Corser

Presentation Overview
Concept Foundation: Barkoky & Charkari (2011) Segmentation Thinning My Contribution: Corser (2012) Segmentation (similar to Barkoky) CED: Canny Edge Dilation (Minus Errors) Assumption: User trains his own phone

Concept Deaf and hearing people talking on the phone, each using their natural language Sign-activated commands like voice-activated

Situation: Drive Thru Window
Think: Stephen Hawking Deaf person signs order Phone speaks order Confirmation on screen

Process Flow Requires several conversion processes
Many have been accomplished Remaining: ASL2TXT

Goal: Find an Algorithm
Find an image processing algorithm that recognizes ASL alphabet = A Web site

Barkoky: Segmentation & Thinning
Barkoky counts endpoints to determine sign (doesn’t work for ASL)

Barkoky Process Segmentation Thinning Capture RGB image Rescale
Extract using colors Reduce noise Crop at wrist Result: hand segment Input: hand segment Apply thinning Find endpoints, joints Calculate lengths Clean short lengths Identify gesture by counting endpoints

1. Capture RGB Image 2. Rescale
% Capture RGB image a = imread('DSC04926.JPG'); figure('Name','RGB image'),imshow(a); % Rescale image to 205x154 a10 = imresize(a, 0.1); figure('Name','Rescaled image'),imshow(a10);

3. Extract Hand Using Colors
% Extract hand using color abw10 = zeros(205,154,1); for i=1:205, for j=1:154, if a10(i,j,2)<140 && a10(i,j,3)<100, abw10(i,j,1)=255; end; figure('Name','Extracted'),imshow(abw10); Note: Color threshold code differs from Barkoky

Colors: Training Set Histograms

Colors: Training Set (2)
Red Green Blue Excel

Colors: Test Set Histograms

4. Reduce Noise % Reduce noise for i=2:204, for j=1:154, if abw10(i-1,j,1)==0 if abw10(i+1,j,1)==0, abw10(i,j,1)=0; end; end; if abw10(i-1,j,1)==255 if abw10(i+1,j,1)==255, end; end; abw10 = imfill(abw10,'holes');

5. Identify Wrist Position
% Identify wrist position for i=204:-1:1, for j=1:154, if abw10(i,j,1)==255, break; end; end; if j ~= 154 && abw10(i+1,j,1)~=255, wristi=i+1; wristj=j+1; break;

Wrist Detection Algorithm searches bottom-to-top of image
Finds a leftmost white pixel above black pixel Sets wrist position SE of found white pixel

Corser: Segmentation & CED
Segmentation (similar to Barkoky) Color threshold technique slightly different American Sign Language (ASL) alphabet, not Persian Sign Language (PSL) numbers Image Comparison: Tried Several Methods Full Threshold (Minus Errors) Diced Segments (Minus Errors) Endpoint Count Difference CED: Canny Edge Dilation

ASL Training Set Hit-or-miss: 23% Barkoky: 8%

ASL Test Set MATLAB

A

A

B

B

C

C

D

D

E

E

F

F

G

G

H

H

I

I

J

J

K

K

L

L

M

M

N

N

O

O

P

P

Q

Q

R

R

S

S

T

T

U

U

V

V

W

W

X

X

Y

Y

Z

Z

Z

Hybrid Algorithm Example
% MATLAB Code matchtotal = 0; if abs(x10range - x20range) < 20, matchtotal = matchtotal + 10; end; if abs(y10range - y20range) < 20, matchtotal = matchtotal + 11; matchtotal = matchtotal - abs(h10 - h20); % h10, h20 are vector magnitudes -----

Erosion Subtraction

Canny Edge

Canny Edge Dilation Code
% MATLAB Code se = strel('disk',5); a10 = edge(a10,'canny'); a20 = edge(a20,'canny'); a10 = imdilate(a10,se); a20 = imdilate(a20,se); % Then calculate matches minus errors

Experimental Results Technique Correct Full Threshold (Minus Errors)
19% (27%) Diced Segments (Minus Errors) 23% (27%) Barkoky Endpoint Count Diff. 8% Hybrid - Height/Width/Endpoints 19% Erosion Subtraction 15% Canny Edge Dilation (Minus Errors) 12% (35%)

Disadvantages Dependent on lighting conditions
Fails with flesh-tone backgrounds Requires calibration to a specific user Limited applications: text messaging, activation (“sign” similar to voice activation) ASL numbers (A=10, D=1, O=0, V=2, W=6) Alphabet is tiny portion of full translation: complete translation maybe many years away

Future Work Barkoky claims flesh tones can be detected, but I have yet to replicate (even Barkoky changed his color detection scheme) Could write letter-by-letter algorithm Could use range camera to compute distance of finger instead of shape of hand Motion analysis or edge count Many possibilities… we’ve only just begun! Cue: music

The End

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