Extracts text from an image. The acronym OCR means 'Optical Character Recognition'.
The following table resumes the configuration properties (access credentials) you must set in order to use this AI task.
| |
PropertyKey |
| ProviderType |
Id |
Key |
SecretKey |
| Alibaba |
- |
用户AccessKey |
用户AccessKey |
| Amazon |
- |
Rekognition |
Rekognition |
| Baidu |
视觉技术 |
视觉技术 |
视觉技术 |
| Google |
- |
Cloud Vision API |
- |
| IBM |
- |
Visual Recognition
(deprecated, dark-beta) |
- |
| Microsoft |
- |
Computer Vision |
- |
| SAP |
- |
Sandbox Environment |
- |
| Tencent |
通用OCR |
通用OCR |
- |
Taking the following image input, the table below shows the text are identified for each provider (as a JSON structure) and the time it takes for processing it.
 |
| Provider |
Output |
|
Benchmark |
| Alibaba |
[{
"label": "LevI'S",
"confidence": 0.999,
"top": 651,
"left": 424,
"width": 128,
"height": 40
}]
|
|
3156ms |
| Amazon |
[{
"label": "LEVI'S",
"confidence": 0.977,
"top": 668,
"left": 428,
"width": 127,
"height": 31
}]
|
|
13531ms |
| Baidu |
[{
"label": "evIs",
"confidence": 1,
"top": 664,
"left": 453,
"width": 108,
"height": 30,
"Info": [{
"property": "finegrained_vertexes_location",
"value": "[{\"x\":453,\"y\":664},{\"x\":476,\"y\":664},
...,{\"x\":453,\"y\":669}]" // 16 points
}, {
"property": "min_finegrained_vertexes_location",
"value": "[{\"x\":453,\"y\":664},{\"x\":559,\"y\":664},
{\"x\":559,\"y\":692},{\"x\":453,\"y\":692}]"
}]
}]
|
|
4335ms |
| Google |
[{
"label": "Levi'S",
"confidence": 1,
"top": 821,
"left": 535,
"width": 139,
"height": 40,
"Info": [{
"property": "Language",
"value": "en"
}
]
}]
|
|
8305ms |
| IBM |
[{
"label": "vrs",
"confidence": 0.697,
"top": 241,
"left": 153,
"width": 8,
"height": 25,
"Info": [{
"property": "line_number",
"value": "0.00000"
}]
}, {
"label": "le",
"confidence": 0.668,
"top": 247,
"left": 162,
"width": 35,
"height": 14,
"Info": [{
"property": "line_number",
"value": "0.00000"
}]
}]
|
|
9251ms |
| Microsoft |
[{
"label": "Levrs",
"confidence": 0.0,
"top": 823,
"left": 538,
"width": 147,
"height": 36,
"Info": [{
"property": "Angle",
"value": "0.00000"
}, {
"property": "Language",
"value": "tr"
}, {
"property": "Orientation",
"value": "Up"
}]
}]
|
|
8496ms |
| SAP |
[{
"label": "Levis",
"confidence": 0.955528974533081,
"top": 820,
"left": 534,
"width": 156,
"height": 39
}]
|
|
8595ms |
| Tencent |
[{
"label": "LeVi's",
"confidence": 0.795,
"top": 668,
"left": 436,
"width": 121,
"height": 28
}]
|
|
7582ms |
Another example, when the input image has clean text (reduced noise).
 |
| Provider |
Output |
|
Benchmark |
| Alibaba |
[{
"label": "GeneXus",
"confidence": 0.999,
"top": 52,
"left": 20,
"width": 63,
"height": 24
}, {
"label": "LEGACY",
"confidence": 0.999,
"top": 70,
"left": 355,
"width": 107,
"height": 25
}, {
"label": "MODERNIZATION",
"confidence": 0.999,
"top": 103,
"left": 356,
"width": 230,
"height": 25
}, {
"label": "WITH GENEXUSTM15",
"confidence": 0.999,
"top": 138,
"left": 357,
"width": 204,
"height": 22
}, {
"label": "DOWNLOAD THE EBOOK!",
"confidence": 0.999,
"top": 228,
"left": 378,
"width": 149,
"height": 14
}]
|
|
4171ms |
| Amazon |
[{
"label": "LEGACY",
"confidence": 0.999,
"top": 103,
"left": 546,
"width": 161,
"height": 41
}, {
"label": "MODERNIZATION",
"confidence": 0.999,
"top": 158,
"left": 550,
"width": 340,
"height": 38
}, {
"label": "WITH",
"confidence": 0.999,
"top": 211,
"left": 546,
"width": 86,
"height": 31
}, {
"label": "15",
"confidence": 0.998,
"top": 211,
"left": 821,
"width": 42,
"height": 35
}, {
"label": "EBOOK!",
"confidence": 0.997,
"top": 350,
"left": 736,
"width": 77,
"height": 20
}, {
"label": "THE",
"confidence": 0.996,
"top": 352,
"left": 695,
"width": 40,
"height": 20
}, {
"label": "ATES",
"confidence": 0.992,
"top": 289,
"left": 364,
"width": 42,
"height": 17
}, {
"label": "GENEXUSTN",
"confidence": 0.983,
"top": 211,
"left": 638,
"width": 167,
"height": 34
}, {
"label": "DOWNLOAD",
"confidence": 0.973,
"top": 350,
"left": 580,
"width": 113,
"height": 20
}, {
"label": "illsion",
"confidence": 0.945,
"top": 95,
"left": 270,
"width": 32,
"height": 11
}, {
"label": "3LD",
"confidence": 0.944,
"top": 308,
"left": 362,
"width": 29,
"height": 15
}, {
"label": "3ut it is!",
"confidence": 0.938,
"top": 134,
"left": 263,
"width": 32,
"height": 8
}, {
"label": "is",
"confidence": 0.933,
"top": 112,
"left": 293,
"width": 9,
"height": 7
}, {
"label": "Genekus:",
"confidence": 0.872,
"top": 92,
"left": 28,
"width": 101,
"height": 24
}, {
"label": "'condinve",
"confidence": 0.863,
"top": 121,
"left": 261,
"width": 38,
"height": 10
}, {
"label": "ian",
"confidence": 0.837,
"top": 98,
"left": 257,
"width": 16,
"height": 7
}]
|
|
2418ms |
| Baidu |
[{
"label": "Genexus",
"confidence": 1.000,
"top": 83,
"left": 30,
"width": 97,
"height": 34,
"Info": [{
"property": "finegrained_vertexes_location",
"value": "[{\"x\":30,\"y\":83},{\"x\":56,\"y\":83},
...,{\"x\":30,\"y\":90}]" // 38 points
}, {
"property": "min_finegrained_vertexes_location",
"value": "[{\"x\":30,\"y\":83},{\"x\":126,\"y\":83},
{\"x\":126,\"y\":116},{\"x\":30,\"y\":116}]"
}]
}, {
"label": "uE it is",
"confidence": 1.000,
"top": 134,
"left": 267,
"width": 45,
"height": 11,
"Info": [{
"property": "finegrained_vertexes_location",
"value": "[{\"x\":267,\"y\":134},{\"x\":273,\"y\":134},
...,{\"x\":267,\"y\":135}]" // 38 points
}, {
"property": "min_finegrained_vertexes_location",
"value": "[{\"x\":267,\"y\":134},{\"x\":311,\"y\":134},
{\"x\":311,\"y\":144},{\"x\":267,\"y\":144}]"
}]
}, {
"label": "EGACY",
"confidence": 1.000,
"top": 107,
"left": 571,
"width": 143,
"height": 42,
"Info": [{
"property": "finegrained_vertexes_location",
"value": "[{"x":571,"y":107},{"x":602,"y":107},
...,{"x":571,"y":116}]" // 38 points
}, {
"property": "min_finegrained_vertexes_location",
"value": "[{\"x\":570,\"y\":107},{\"x\":711,\"y\":107},
{\"x\":711,\"y\":147},{\"x\":570,\"y\":147}]"
}
]
}, {
"label": "MODERNIZATION",
"confidence": 1.000,
"top": 157,
"left": 545,
"width": 363,
"height": 44,
"Info": [{
"property": "finegrained_vertexes_location",
"value": "[{\"x\":545,\"y\":157},{\"x\":577,\"y\":157},
...,{\"x\":545,\"y\":168}]" // 38 points
}, {
"property": "min_finegrained_vertexes_location",
"value": "[{\"x\":545,\"y\":157},{\"x\":906,\"y\":157},
{\"x\":906,\"y\":199},{\"x\":545,\"y\":199}]"
}]
}, {
"label": "WITH GENEXUSM 15",
"confidence": 1.000,
"top": 213,
"left": 545,
"width": 322,
"height": 35,
"Info": [{
"property": "finegrained_vertexes_location",
"value": "[{\"x\":545,\"y\":213},{\"x\":571,\"y\":213},
...,{\"x\":545,\"y\":220}]" // 38 points
}, {
"property": "min_finegrained_vertexes_location",
"value": "[{\"x\":545,\"y\":213},{\"x\":865,\"y\":213},
{\"x\":865,\"y\":247},{\"x\":545,\"y\":247}]"
}]
}, {
"label": "DOWNLOAD THE EBOOK!",
"confidence": 1.000,
"top": 354,
"left": 583,
"width": 232,
"height": 21,
"Info": [{
"property": "finegrained_vertexes_location",
"value": "[{\"x\": 583,\"y\": 354},{\"x\": 598,\"y\": 354},
...,{\"x\": 583,\"y\": 359}]" // 38 points
}, {
"property": "min_finegrained_vertexes_location",
"value": [{\"x\":582,\"y\":353},{\"x\":812,\"y\":353},
{\"x\":812,\"y\":373},{\"x\":582,\"y\":373}]"
}]
}]
|
|
2418ms |
| Google |
[{
"label": "LEGACY",
"confidence": 1,
"top": 110,
"left": 551,
"width": 155,
"height": 34,
"Info": [{
"property": "Language",
"value": ""
}
]
}, {
"label": "MODERNIZATION",
"confidence": 1,
"top": 162,
"left": 553,
"width": 347,
"height": 35,
"Info": [{
"property": "Language",
"value": ""
}
]
}, {
"label": "WITH",
"confidence": 1,
"top": 216,
"left": 553,
"width": 77,
"height": 29,
"Info": [{
"property": "Language",
"value": ""
}
]
}, {
"label": "GENEXUS",
"confidence": 1,
"top": 216,
"left": 641,
"width": 150,
"height": 29,
"Info": [{
"property": "Language",
"value": ""
}
]
}, {
"label": "15",
"confidence": 1,
"top": 216,
"left": 823,
"width": 31,
"height": 29,
"Info": [{
"property": "Language",
"value": ""
}
]
}, {
"label": "Mlusion",
"confidence": 1,
"top": 99,
"left": 274,
"width": 24,
"height": 7,
"Info": [{
"property": "Language",
"value": ""
}
]
}, {
"label": "GeneXus",
"confidence": 1,
"top": 96,
"left": 34,
"width": 85,
"height": 3,
"Info": [{
"property": "Language",
"value": ""
}
]
}, {
"label": "it",
"confidence": 1,
"top": 136,
"left": 279,
"width": 4,
"height": 5,
"Info": [{
"property": "Language",
"value": ""
}
]
}, {
"label": "is!",
"confidence": 1,
"top": 136,
"left": 287,
"width": 8,
"height": 7,
"Info": [{
"property": "Language",
"value": ""
}
]
}, {
"label": "(0S",
"confidence": 1,
"top": 262,
"left": 291,
"width": 9,
"height": 7,
"Info": [{
"property": "Language",
"value": ""
}
]
}, {
"label": "430",
"confidence": 1,
"top": 263,
"left": 304,
"width": 9,
"height": 7,
"Info": [{
"property": "Language",
"value": ""
}
]
}, {
"label": "ees",
"confidence": 1,
"top": 263,
"left": 318,
"width": 16,
"height": 8,
"Info": [{
"property": "Language",
"value": ""
}
]
}, {
"label": "ATES",
"confidence": 1,
"top": 295,
"left": 369,
"width": 31,
"height": 18,
"Info": [{
"property": "Language",
"value": ""
}
]
}, {
"label": "RLD",
"confidence": 1,
"top": 313,
"left": 367,
"width": 20,
"height": 13,
"Info": [{
"property": "Language",
"value": ""
}
]
}, {
"label": "DOWNLOAD",
"confidence": 1,
"top": 356,
"left": 586,
"width": 106,
"height": 15,
"Info": [{
"property": "Language",
"value": ""
}
]
}, {
"label": "THE",
"confidence": 1,
"top": 356,
"left": 698,
"width": 36,
"height": 15,
"Info": [{
"property": "Language",
"value": ""
}
]
}, {
"label": "EBOOK!",
"confidence": 1,
"top": 356,
"left": 741,
"width": 69,
"height": 15,
"Info": [{
"property": "Language",
"value": ""
}
]
}]
|
|
2716ms |
| IBM |
[{
"label": "gene",
"confidence": 0.7228,
"top": 92,
"left": 35,
"width": 62,
"height": 27,
"Info": [{
"property": "line_number",
"value": "0.00000"
}]
}, {
"label": "nexus",
"confidence": 0.8237,
"top": 80,
"left": 62,
"width": 68,
"height": 32,
"Info": [{
"property": "line_number",
"value": "0.00000"
}]
}, {
"label": "legacy",
"confidence": 0.9684,
"top": 111,
"left": 553,
"width": 158,
"height": 37,
"Info": [{
"property": "line_number",
"value": "1.00000"
}]
}, {
"label": "modernization",
"confidence": 0.9898,
"top": 163,
"left": 553,
"width": 351,
"height": 37,
"Info": [{
"property": "line_number",
"value": "2.00000"
}]
},
...
}]
|
|
12898ms |
| Microsoft |
[{
"label": "GeneXus",
"confidence": 0,
"top": 94,
"left": 0,
"width": 126,
"height": 76,
"Info": [{
"property": "Angle",
"value": "0.00000"
}, {
"property": "Language",
"value": "en"
}, {
"property": "Orientation",
"value": "up"
}
]
}, {
"label": "WITH GENEXUSTM 15",
"confidence": 0,
"top": 216,
"left": 0,
"width": 862,
"height": 215,
"Info": [{
"property": "Angle",
"value": "0.00000"
}, {
"property": "Language",
"value": "en"
}, {
"property": "Orientation",
"value": "up"
}
]
}, {
"label": "DOWNLOAD THE EBOOK!",
"confidence": 0,
"top": 353,
"left": 0,
"width": 813,
"height": 352,
"Info": [{
"property": "Angle",
"value": "0.00000"
}, {
"property": "Language",
"value": ""
}, {
"property": "Orientation",
"value": ""
}
]
},
...
]
|
|
8496ms |
| SAP |
[{
"label": "MODERNIZATION",
"confidence": 0.995006024837494,
"top": 159,
"left": 562,
"width": 322,
"height": 41
}, {
"label": "LEGACY",
"confidence": 0.953990995883942,
"top": 109,
"left": 551,
"width": 161,
"height": 37
}, {
"label": "Genexus",
"confidence": 0.974162995815277,
"top": 79,
"left": 30,
"width": 99,
"height": 40
}, {
"label": "DOWNLOAD",
"confidence": 0.931997001171112,
"top": 355,
"left": 586,
"width": 106,
"height": 18
}, {
"label": "GENEXUS",
"confidence": 0.976303994655609,
"top": 216,
"left": 641,
"width": 155,
"height": 30
}, {
"label": "WITH",
"confidence": 0.947585999965668,
"top": 217,
"left": 550,
"width": 83,
"height": 27
}, {
"label": "RATES",
"confidence": 0.932789027690888,
"top": 294,
"left": 364,
"width": 39,
"height": 19
}, {
"label": "EBOOK!",
"confidence": 0.912930011749268,
"top": 354,
"left": 742,
"width": 71,
"height": 19
}, {
"label": "RLD",
"confidence": 0.899074018001556,
"top": 310,
"left": 362,
"width": 28,
"height": 18
}, {
"label": "THE",
"confidence": 0.899158000946045,
"top": 356,
"left": 698,
"width": 39,
"height": 16
}, {
"label": "15",
"confidence": 0.925707995891571,
"top": 217,
"left": 826,
"width": 36,
"height": 29
}]
|
|
3780ms |
| Tencent |
[{
"label": "15",
"confidence": 0.99,
"top": 232,
"left": 856,
"width": 37,
"height": 34
}, {
"label": "MODE",
"confidence": 0.975,
"top": 242,
"left": 577,
"width": 96,
"height": 70
}, {
"label": "RLD",
"confidence": 0.96,
"top": 480,
"left": 452,
"width": 22,
"height": 13
}, {
"label": "RATES",
"confidence": 0.955,
"top": 461,
"left": 447,
"width": 36,
"height": 17
}, {
"label": "GENF",
"confidence": 0.943,
"top": 277,
"left": 685,
"width": 63,
"height": 46
}, {
"label": "THEE",
"confidence": 0.873,
"top": 396,
"left": 780,
"width": 38,
"height": 44
}, {
"label": "ERi",
"confidence": 0.868,
"top": 229,
"left": 675,
"width": 52,
"height": 50
}, {
"label": "NEXUS\"",
"confidence": 0.863,
"top": 242,
"left": 746,
"width": 89,
"height": 68
}, {
"label": "NZATON",
"confidence": 0.848,
"top": 159,
"left": 735,
"width": 180,
"height": 103
}, {
"label": "LEGAER",
"confidence": 0.828,
"top": 174,
"left": 559,
"width": 149,
"height": 94
}, {
"label": "?",
"confidence": 0.775,
"top": 350,
"left": 120,
"width": 29,
"height": 42
}, {
"label": "Gene",
"confidence": 0.775,
"top": 369,
"left": 68,
"width": 46,
"height": 37
}, {
"label": "EBDON",
"confidence": 0.759,
"top": 372,
"left": 823,
"width": 67,
"height": 46
}, {
"label": "MITHC",
"confidence": 0.712,
"top": 300,
"left": 591,
"width": 87,
"height": 59
}, {
"label": "auters",
"confidence": 0.71,
"top": 318,
"left": 288,
"width": 33,
"height": 12
}, {
"label": "OMCOO",
"confidence": 0.614,
"top": 411,
"left": 677,
"width": 97,
"height": 58
}, {
"label": "HhIsion",
"confidence": 0.602,
"top": 308,
"left": 294,
"width": 27,
"height": 9
}]
|
|
1827ms |
- The label assigned for an object depends on the provider used (i.e. string detection accuracy). Additional information can be found on the OutputRegion.Info field if it is given by the provider.
- Some providers are more accurate with noisy images than others (e.g. stamps on shirts). All of them work fine with clean text on the image (e.g. a scanned text).
- Maximum image file size is 10MB.
- GeneXusAI does not provide support for drawing a rectangle over an image. This action is the responsibility of the developer.
TIP: For Web applications, a good alternative can be combining HTML5 Canvas control with JavaScript with User Control object. On the other hand, for Smart Devices you could use Image Map control on which you can set the processed image as background and 'draw' square regions (i.e. set a border color on the table item of the grid).
- IBM provider: Deprecated as of September 12 (2019). Check IBM Visual Recognition's Release Notes. Before deprecation, GeneXusAI sses the dark beta feature of Visual Recognition and you had to request access.
| Platforms |
Web(.NET,.NETCore,Java), SmartDevices(Android,iOS) |
| Connectivity |
Online |
This procedure is available as of GeneXus 16.
|