我正在使用Microsoft Computer Vision讀取收據。處理OCR /計算機視覺結果以匹配收據結構
我得到的結果被排序到按列分組的區域中,例如,數量,產品名稱,數量在三個不同的地區。
我寧願如果整個產品列表是一個區域,並且每一行都是產品。
是否有任何方法來配置計算機視覺來完成此任務,或者更有可能是因爲所有單詞的位置都可用,所以可以使用後處理結果中的任何好技術或庫。
貝婁是收據和計算機視覺效果的圖像。
{
"language": "sv",
"textAngle": 2.0999999999999632,
"orientation": "Up",
"regions": [
{
"boundingBox": "1012,450,660,326",
"lines": [
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"boundingBox": "1362,450,76,30",
"words": [
{
"boundingBox": "1362,450,76,30",
"text": "JULA"
}
]
},
{
"boundingBox": "1207,486,465,49",
"words": [
{
"boundingBox": "1207,502,172,33",
"text": "Ekslinsan"
},
{
"boundingBox": "1400,497,51,30",
"text": "3B,"
},
{
"boundingBox": "1479,491,95,33",
"text": "25467"
},
{
"boundingBox": "1595,486,77,32",
"text": "VALA"
}
]
},
{
"boundingBox": "1304,539,265,38",
"words": [
{
"boundingBox": "1304,539,265,38",
"text": "SE5S6944785601"
}
]
},
{
"boundingBox": "1245,584,369,44",
"words": [
{
"boundingBox": "1245,594,148,34",
"text": "Telefon:"
},
{
"boundingBox": "1421,584,193,37",
"text": "042-324040"
}
]
},
{
"boundingBox": "1012,695,269,35",
"words": [
{
"boundingBox": "1012,702,75,28",
"text": "Kund"
},
{
"boundingBox": "1109,695,172,33",
"text": "072202787"
}
]
},
{
"boundingBox": "1109,738,289,38",
"words": [
{
"boundingBox": "1109,744,133,32",
"text": "LILLVIK"
},
{
"boundingBox": "1265,738,133,32",
"text": "ANDREAS"
}
]
}
]
},
{
"boundingBox": "1085,845,14,516",
"lines": [
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"words": [
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"boundingBox": "1090,845,9,29",
"text": "1"
}
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"text": "1"
}
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"words": [
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"boundingBox": "1086,1133,9,27",
"text": "I"
}
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"words": [
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"text": "1"
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]
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"lines": [
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"words": [
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"boundingBox": "1128,843,36,29",
"text": "ST"
},
{
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"text": "661107"
}
]
},
{
"boundingBox": "1127,879,389,41",
"words": [
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"boundingBox": "1127,887,232,33",
"text": "VERKTYGSLÅDR"
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{
"boundingBox": "1382,883,36,28",
"text": "JC"
},
{
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"text": "5"
},
{
"boundingBox": "1481,879,35,28",
"text": "ÅR"
}
]
},
{
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"words": [
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"text": "ST"
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{
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"text": "181460"
}
]
},
{
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"words": [
{
"boundingBox": "1126,987,75,30",
"text": "BORR"
},
{
"boundingBox": "1224,977,193,35",
"text": "GLAS/KRKEL"
},
{
"boundingBox": "1440,974,16,27",
"text": "ø"
},
{
"boundingBox": "1482,971,34,27",
"text": "10"
},
{
"boundingBox": "1539,967,37,28",
"text": "MM"
}
]
},
{
"boundingBox": "1125,1027,173,37",
"words": [
{
"boundingBox": "1125,1036,36,28",
"text": "ST"
},
{
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"text": "181740"
}
]
},
{
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"words": [
{
"boundingBox": "1124,1071,252,40",
"text": "UNIVERSALBORR"
},
{
"boundingBox": "1400,1066,96,32",
"text": "8X120"
},
{
"boundingBox": "1519,1062,37,30",
"text": "MM"
}
]
},
{
"boundingBox": "1123,1125,175,34",
"words": [
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"boundingBox": "1123,1129,36,30",
"text": "ST"
},
{
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"text": "181738"
}
]
},
{
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"words": [
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"text": "UNIVERSRLBORR"
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"text": "MM"
}
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"text": "ST"
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"text": "316401"
}
]
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{
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"words": [
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"text": "LÅSCYLINDER"
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}
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"text": "LÅSCYLINDER"
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"text": "2-PRCK"
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]
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}
]
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"text": "44,"
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"text": "90"
}
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},
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"words": [
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"text": "69,"
},
{
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"text": "90"
}
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},
{
"boundingBox": "1661,1103,97,35",
"words": [
{
"boundingBox": "1661,1103,97,35",
"text": "49,90"
}
]
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{
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"words": [
{
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"text": "299,00"
}
]
}
]
},
{
"boundingBox": "1064,1469,620,45",
"lines": [
{
"boundingBox": "1064,1469,620,45",
"words": [
{
"boundingBox": "1064,1481,237,33",
"text": "-Rabattcheck"
},
{
"boundingBox": "1324,1486,51,24",
"text": "nr:"
},
{
"boundingBox": "1384,1469,300,38",
"text": "935011035567095"
}
]
}
]
},
{
"boundingBox": "1123,1584,159,82",
"lines": [
{
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"words": [
{
"boundingBox": "1123,1584,159,33",
"text": "DELSUMMA"
}
]
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{
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"words": [
{
"boundingBox": "1143,1635,116,31",
"text": "Rabatt"
}
]
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{
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"lines": [
{
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"words": [
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"text": "|"
},
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"text": "041,70"
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"words": [
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"text": "50,00"
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]
},
{
"boundingBox": "1651,1725,120,34",
"words": [
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"text": "991"
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{
"boundingBox": "1715,1746,9,13",
"text": ","
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"text": "70"
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"words": [
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"words": [
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"text": "Totalbelopp"
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"text": "/"
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"text": "70"
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{
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"words": [
{
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"text": "Säljare:"
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{
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"text": "7688"
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]
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]
},
{
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"lines": [
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"words": [
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"text": "Ex"
},
{
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"text": "Moms"
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]
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]
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]
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{
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"words": [
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"text": "Moms"
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"text": "%"
}
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{
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"words": [
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"text": "25,00"
}
]
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]
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"lines": [
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"words": [
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"text": "Moms"
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]
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{
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"lines": [
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"words": [
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"text": "7618"
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"words": [
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"text": "Sabina"
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"words": [
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"text": "24"
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{
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"text": "oktober"
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{
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"text": "2016"
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"words": [
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"text": "44601"
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{
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"text": "Kvitto:"
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{
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"text": "51756"
}
]
},
{
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"words": [
{
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"text": "Kassa"
}
]
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{
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"words": [
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"text": "Spara"
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{
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"text": "kvittot,"
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{
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"text": "gäller"
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{
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"text": "som"
},
{
"boundingBox": "1657,3158,167,39",
"text": "garanti."
}
]
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{
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"words": [
{
"boundingBox": "1268,3217,103,39",
"text": "Öppet"
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{
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"text": "köp"
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{
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"text": "30"
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{
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"text": "dager"
}
]
},
{
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"words": [
{
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"text": "VÄLKOMMEN"
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{
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"text": "ÅTER!"
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]
},
{
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"words": [
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"text": "Om"
},
{
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"text": "ditt"
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"text": "namn"
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{
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"text": "och"
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{
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"text": "personnummer"
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{
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"text": "har"
}
]
},
{
"boundingBox": "1032,3395,894,42",
"words": [
{
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"text": "lämnats"
},
{
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"text": "för"
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{
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"text": "att"
},
{
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"text": "genomföra"
},
{
"boundingBox": "1596,3399,61,36",
"text": "ett"
},
{
"boundingBox": "1685,3399,241,38",
"text": "JulaPro-köp"
}
]
},
{
"boundingBox": "985,3455,966,42",
"words": [
{
"boundingBox": "985,3456,193,37",
"text": "behandlar"
},
{
"boundingBox": "1203,3455,85,37",
"text": "Jula"
},
{
"boundingBox": "1312,3456,84,37",
"text": "dina"
},
{
"boundingBox": "1421,3458,195,39",
"text": "uppgifter"
},
{
"boundingBox": "1645,3462,12,33",
"text": "i"
},
{
"boundingBox": "1686,3458,173,38",
"text": "enlighet"
},
{
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"text": "med"
}
]
}
]
}
]
}
你找到解決方案? – RAVI
我們提取所有將它們放在一個列表中的單詞。稍後我們將使用Azure機器學習來確定屬於哪個屬性以及它屬於哪種屬性。但現在我們只是使用單獨的單詞的邊框來創建可點擊的區域,用戶需要爲不同的屬性選擇正確的框。 – Lillvik