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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

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**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

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

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**Situation: Drive Thru Window**

Think: Stephen Hawking Deaf person signs order Phone speaks order Confirmation on screen

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**Process Flow Requires several conversion processes**

Many have been accomplished Remaining: ASL2TXT

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**Goal: Find an Algorithm**

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

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**Barkoky: Segmentation & Thinning**

Barkoky counts endpoints to determine sign (doesn’t work for ASL)

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**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

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**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);

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**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

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**Colors: Training Set Histograms**

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**Colors: Training Set (2)**

Red Green Blue Excel

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**Colors: Test Set Histograms**

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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');

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**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;

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**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

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**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

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

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ASL Test Set MATLAB

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A

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A

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B

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C

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D

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D

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P

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**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 -----

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Erosion Subtraction

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Canny Edge

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**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

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**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%)

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**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

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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

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The End

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