多類別的影像語意分割 multi-class semantic segmentation
透過Labelme標註的訓練資料：
令人驚豔的模型推論結果：
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OCR Application Example1: for SMD idenfication : Text detect by CRAFT OCR文字偵測 原始照片為網路上下載，再套上OCR文字偵測顯示結果，若有侵權請告知移除 彩色區域為偵測到文字的部份 Output 10 coordinates of corresponding text blocks 1. 144,196,286,194,287,259,145,261 2. 298,198,509,196,509,259,298,262 3. 148,262,286,262,286,321,148,321 4. 368,266,513,264,513,321,369,323 5. 145,331,472,333,471,395,145,393 6. 146,404,445,404,445,454,146,454 7. 146,453,512,453,512,502,146,502 8. 147,502,481,499,481,551,148,553 9. 148,550,614,550,614,600,148,600 10.513,600,714,600,714,648,513,648 After image pre-processing: OCR result1: After image pre-processing: OCR result2: Example2: for datasheet interpretation : Text detect of TI datasheet by CRAFT OCR results: ([[75, 11], [127, 11], [127, 31], [75, 31]], 'TEXAS', 0.999188403930061) ([[474, 4], [928, 4], [928, 32], [474, 32]], 'PACKAGE MATERIALS INFORMATION', 0.6743955072876302) ([[77, 29],...
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