OCR REST API · WorkFusion Documentation

API reference

WorkFusion 10.0.0

General information

Primary APIs:

/api/v1/cloud/submitImage
/api/v1/cloud/processDocument
/api/v1/cloud/getTaskStatus

tip

Authentication

Like some other components of IA Cloud Enterprise, Optical Character Recognition (OCR) uses JWT for authentication. To access most OCR resources, you need to retrieve the JWT token. For that, you can access any Workfusion instance, provide a user login/password and retrieve a short-living JWT token:

curl -X POST -H Content-Type:application/json -d '{"username":"'<USER>","password":"<PASSWORD>"}' http://<workfusion_hostname>/workfusion/api/v1/jwt/login

You can use that token to access any OCR resource, for example:

curl --form "file=@FILENAME" -H "Authorization:Bearer <JWT_TOKEN>" "http://<ocr-hostname>/api/v1/cloud/processImage"

Using command line

Process image: specifies an appropriate location of a file (FILENAME) and OCR parameters to processImage API call.

curl -s --form "file=@FILENAME" "http://ocr.hostname:8080/api/v1/cloud/processImage?correctSkew=true&xml:writeRecognitionVariants=false&profile=documentConversion&exportFormat=txt&language=English&correctOrientation=true"

Output: copies a task ID for the next command.

<?xml version="1.0" encoding="UTF-8" standalone="yes"?><response><task id="5693d8f77b78005fcbbfbe84" message="OK" processEndTime="2016-01-11T16:32:00" processStartTime="2016-01-11T16:31:56" registrationTime="2016-01-11T16:32:28" resultUrl="http://ocr.hostname:8080/api/v1/cloud/download?taskId=5693d8f77b78005fcbbfbe84&amp;result=1" resultUrl2="http://ocr.hostname:8080/api/v1/cloud/download?taskId=5693d8f77b78005fcbbfbe84&amp;result=2" resultUrl3="http://ocr.hostname:8080/api/v1/cloud/download?taskId=5693d8f77b78005fcbbfbe84&amp;result=3" statusChangeTime="2016-01-11T16:32:28" status="Completed"/></response>

Gets status by the task ID:

curl -s "http://ocr.hostname:8080/api/v1/cloud/getTaskStatus?taskId=5693d8f77b78005fcbbfbe84"

Output: If message="OK", you can download results from links in resultUrl,resultUrl2,resultUrl3 (depends on the export format).

<?xml version="1.0" encoding="UTF-8" standalone="yes"?><response><task id="5693d8f77b78005fcbbfbe84" message="OK" processEndTime="2016-01-11T16:32:00" processStartTime="2016-01-11T16:31:56" registrationTime="2016-01-11T16:32:28" resultUrl="http://ocr.hostname:8080/api/v1/cloud/download?taskId=5693d8f77b78005fcbbfbe84&amp;result=1" resultUrl2="http://ocr.hostname:8080/api/v1/cloud/download?taskId=5693d8f77b78005fcbbfbe84&amp;result=2" resultUrl3="http://ocr.hostname:8080/api/v1/cloud/download?taskId=5693d8f77b78005fcbbfbe84&amp;result=3" statusChangeTime="2016-01-11T16:32:28" status="Completed"/></response>

API Usage

API sequence call

Uploads file with submitImage.

Copies taskId from the response:

<?xml version="1.0" encoding="UTF-8" standalone="yes"?>
<response>
    <task id="5b06b1b295ac0b000144fb7e" processEndTime="" processStartTime="" registrationTime="2018-05-24T12:36:02" 
resultUrl="http://ocr2-dev2.crowdcomputingsystems.com:8080/api/v1/cloud/download?taskId=5b06b1b295ac0b000144fb7e&amp;
result=1" resultUrl2="http://ocr2-dev2.crowdcomputingsystems.com:8080/api/v1/cloud/download?taskId=5b06b1b295ac0b000144fb7e&amp;
result=2" resultUrl3="http://ocr2-dev2.crowdcomputingsystems.com:8080/api/v1/cloud/download?taskId=5b06b1b295ac0b000144fb7e&amp;
result=3" statusChangeTime="2018-05-24T12:36:02" 
status="Submitted"/>
</response>

Starts image processing with processDocument.
Checks task status with getTaskStatus (specify correct taskId).

If the task is complete (status="Completed"), open results from the resultUrl, resultUrl2 or resultUrl3 fields (replace & with &).

Custom request parameters

We implement custom or additional request parameters. See the following sections.

GET /processDocument

exportFormat: OCR API export format:
- html: HTML page
- pdfSearchable: text can be searched in this type of file
- xml: file contains characters or words along with their location in the original document (coordinates or frames)
- xmlForCorrectedImage: the same as xml, except location is taken from a processed or adjusted document
- txt: plain text (default)
For multiple export formats, you can use combinations delimited by a comma: pdfSearchable,xmlForCorrectedImage,html. You can use maximum of three types at once.

Attention: You can use maximum three types at once.

customRegions (JSON variable)

[  
   {  
      "type":"BT_Table",
      "page":1,
      "left":0,
      "top":0,
      "right":2000,
      "bottom":2000
   },
   {  
      "type":"BT_Table",
      "page":1,
      "left":0,
      "top":2000,
      "right":4000,
      "bottom":4000
   },
   {  
      "type":"BT_Table",
      "page":2,
      "left":0,
      "top":0,
      "right":4000,
      "bottom":4000
   }
]

The example to select one table region for all pages.

[
    {
        "type": "BT_Table",
        "left": 0,
        "top": 0,
        "right": 2000,
        "bottom": 2000
    }
]

correctSkew (default true): if true, the page skew will be detected and automatically corrected.
correctOrientation (default true): if true, the page orientation will be detected and if it differs from normal, will be automatically rotated.
skipPreprocessing (default false) skips preprocessing stage. It will increase performance up to 30%.
useOnlyCustomRegions (default false) skips original analyzing stage / extract information from custom regions only.
alphabetExtension: string with specials symbols to extend already defined alphabet.
useDefaultPattern (default false): if true, it requires to apply the default pattern (from the OCR application bundle).
removeNoiseModels removes noise on the image (optional, valid value - comma separated values: CorrelatedNoise, WhiteNoise). Important: This method can be used for color and 8-bit gray images only.
removeGarbageSize removes garbage (excess dots that are smaller than a certain size) from the image (optional, valid value > 0 and -1 for automatically detect garbage size).
allowedRegionTypes: allowed region types for identified blocks classification. If allowedRegionTypes=Empty, all types will be processed. For example, to suppress classifying any block as picture (BT_RasterPicture) specify the parameter value as BT_Table, BT_Text, BT_Barcode, BT_VectorPicture, BT_Separator, BT_SeparatorGroup, BT_Checkmark, BT_CheckmarkGroup. NOTE: Narrowing down type of regions can break page layout. Do not use it if you're not sure you need it.
discardColorImage: if you work with black-and-white images or the color of images is not important, set the discardColorImage to true.
enhanceLocalContrast specifies whether the local contrast of the image should be increased. Such preprocessing may increase the quality of recognition. It is effective for:
- photos or scans of documents with texture or pictures in the background
- photos or scans of documents with highly colorful background or text highlighting
language specifies predefined language, for example, English. You can also define multiple languages and use comma as a separator: English,German,Polish.
lowResolutionMode (true, false) improves recognition of images with low resolution, for example, faxes.

Pre-processing parameters:

invertImage (default false) inverts an image.
discardColorImage (default false) leaves only black-and-white plane in the prepared image.
removeColorObjects removes color objects from the image, colors values: Blue, Green, Red, Yellow.
removeColorObjectsType (default Background) removes color objects from the image, modes values: Background, Full, Stamp.
convertTo (value=TIFF): automatic detection of the file type and conversion to TIFF (convert before processing). Accepts images: PDF, PNG, JPG, JPEG.
pages: selection of pages from PDF files for recognition, for example, pages=1,2,3,10-15.
changeDPI contains the new value of DPI. Available values: from 50 to 3,200, for example, changeDPI=300.
useAutoDetectedDPIFromRange ({"min": 50,"max": 3200}) is DPI detection from the defined range to detect best resolution and apply it before further processing. Note: changeDPI is not applicable when useAutoDetectedDPIFromRange is defined. When engine cannot define best DPI then original DPI is used.
priority: (0-10) defines priority in the image processing queue.

POST /processImage

file: file in one of the following formats: PDF, PNG, JPG, JPEG
exportFormat: OCR API export format:
- html: HTML page
- pdfSearchable: text can be searched in such a file
- xml: file contains characters or words along with their location in the original document (coordinates and frames)
- xmlForCorrectedImage: the same as XML, except location is taken from a processed or adjusted document
- txt: plain text (default)
For multiple export formats, you can use combinations delimited by the comma: pdfSearchable,xmlForCorrectedImage,html. You can use maximum of three types at once.
xml:writeRecognitionVariants makes XML and xmlForCorrectedImage formats to contain all variants of character or word OCR considered as a possible recognition.
customRegions (JSON variable).
correctSkew (default true): if true, the page skew will be detected and automatically corrected.
correctOrientation (default true): if true, the page orientation will be detected and if it differs from normal, will be automatically rotated.
skipPreprocessing (default false).
useOnlyCustomRegions (default false) skips original analyzing stage / extract information from custom regions only.
alphabetExtension: string with specials symbols to extend already defined alphabet.
pattern: file upload for a pattern to be applied to recognize special symbols. Note:
- You should also add the symbol to alphabetExtension.
- You can use either the explicit pattern uploaded with the field or useDefaultPattern=true. Both cannot be used at a time.
useDefaultPattern (default false): if true, it requires to apply the default pattern (from the OCR application bundle).
dictionary: file where each line contains a word or combination of characters which can be used to improve OCR recognition. The set of words extends, not limits, the default dictionary.
removeNoiseModels removes noise on the image (optional, valid value - comma separated values: CorrelatedNoise, WhiteNoise). Important: This method can be used for color and 8-bit gray images only.
removeGarbageSize removes garbage (excess dots that are smaller than a certain size) from the image (optional, valid value > 0).
allowedRegionTypes: allowed region types for identified blocks classification. If allowedRegionTypes=Empty, all types will be processed. For example, to suppress classifying any block as picture (BT_RasterPicture) specify the parameter value as BT_Table, BT_Text, BT_Barcode, BT_VectorPicture, BT_Separator, BT_SeparatorGroup, BT_Checkmark, BT_CheckmarkGroup. To narrow down type of regions even more you can use the following set: BT_Table, BT_Text, BT_Separator, BT_SeparatorGroup. NOTE: Narrowing down type of regions can break page layout. Do not use it if you're not sure you need it.
enhanceLocalContrast specifies whether the local contrast of the image should be increased. Such preprocessing may increase the quality of recognition. It is effective for:
- photos or scans of documents with texture or pictures in the background
- photos or scans of documents with highly colorful background or text highlighting
language specifies predefined language, for example, English.
lowResolutionMode (true,false) improves recognition of images with low resolution, for example, faxes.

Pre-processing parameters:

invertImage (default false) inverts image colors.
discardColorImage(default false) leaves only black-and-white plane in the prepared image.

important

The parameter discards a color plane of Document. Therefore, it is incompatible with the removeColorObjects parameter.
removeColorObjects removes color objects from the image, colors values: Blue, Green, Red, Yellow.
removeColorObjectsType specifies the type of the objects to be removed. Supported types: Full, Background, Stamp.
- Full: all color objects on the image
- Background: color objects in the background
- Stamp: only color stamps and signatures.
important

The method can be used for color images only. Also, you can use only one type per request.
convertTo (value=TIFF): autodetection of the file type and conversion to TIFF (convert before processing). Accepts images: PDF, PNG, JPG, JPEG.
pages: selection of pages from PDF files for recognition (example:pages=1,2,3,10-15).
changeDPI contains the new value of dpi, changeDPI available values from 50 to 3200 (example:changeDPI=300).
useAutoDetectedDPIFromRange: ({"min": 50,"max": 3200}) DPI detection from defined range to detect best resolution and apply it before further processing. Note:changeDPI is not applicable when useAutoDetectedDPIFromRange is defined. When engine cannot define best DPI then original DPI is used.
priority (0-10) defines priority in the image processing queue.

Custom APIs

GET /summary

Returns the total number of tasks with status QUEUED. Can produce JSON and XML.

GET /cancelTasks

Change status for all NEW, QUEUED and INPROGRESS tasks to CANCELLED.

Response example:

tip

Add the new parameter that defines which tasks to cancel. For example, the upload date or task ID.

POST /trainPattern

Trains a user pattern for the symbol.

file: image file with a symbol
baseLine contains the distance from the base line to the top edge of the cropped image of the character. The base line is the line on which the characters are located.
The top edge of the image is determined by the character orientation. H1 on the picture.
smallSymbolHeight specifies the height of small characters in pixels on the source image. H2 on the picture.
symbol: symbol associated with pictures
mergePattern: pattern uploaded as a file. If provided, the training output pattern will be combined with the uploaded.

Request example:

Response is a downloadable pattern file in a proprietary binary format.

note

When using a custom pattern, you need to specify alphabetExtension with the trained symbol in processImage or processDocument.

POST /submitPattern

The method attaches a pattern file for recognition of special symbols to an existing task created with submitImage. To be consequently processed with the processDocument action.

Parameters:

pattern: the pattern file to upload
taskId: mandatory

GET /api/project-info

GET /activeLicense

Returns the current active license and all available information about this license.

Result example

{
   "availableTextTypes":    [
      "ATT_Normal",
      "ATT_Typewriter",
      "ATT_Matrix",
      "ATT_Index",
      "ATT_OCR_A",
      "ATT_OCR_B",
      "ATT_MICR_E13B",
      "ATT_MICR_CMC7",
      "ATT_Advanced"
   ],
   "availableBarcodeModules":    [
      "ABM_1D",
      "ABM_PDF417",
      "ABM_Aztec",
      "ABM_QRCode",
      "ABM_MaxiCode",
      "ABM_DataMatrix",
      "ABM_Autolocation"
   ],
   "availableEngineModules":    [
      "AEM_ProcessAsPlainText",
      "AEM_Process",
      "AEM_Analyze",
      "AEM_Recognize",
      "AEM_Synthesize",
      "AEM_ExtendedCharacterInfo",
      "AEM_OpenPDF",
      "AEM_UserPatterns",
      "AEM_BalancedMode",
      "AEM_FastMode",
      "AEM_BCR",
      "AEM_Classification"
   ],
   "availableExportFormats":    [
      "AEF_RTF",
      "AEF_HTML",
      "AEF_XLS",
      "AEF_PDF",
      "AEF_Text",
      "AEF_PDFImageOnly",
      "AEF_XML",
      "AEF_PPT",
      "AEF_PDFA",
      "AEF_PDFMRC",
      "AEF_ALTO",
      "AEF_EPUB",
      "AEF_FB2",
      "AEF_ODT",
      "AEF_XPS"
   ],
   "availableVisualComponents": [],
   "availableLanguageSets":    [
      "ALS_Standard",
      "ALS_DataCapture",
      "ALS_Artificial",
      "ALS_Programming",
      "ALS_User",
      "ALS_Chinese",
      "ALS_Hebrew",
      "ALS_Thai",
      "ALS_Vietnamese",
      "ALS_Arabic",
      "ALS_Japanese",
      "ALS_Korean"
   ],
   "volumeRefreshingPeriod": "VRP_Infinite",
   "volume": 10000,
   "volumeRemaining": 10000,
   "serialNumber": "SWAT-1101-1004-1541-1060-5817",
   "allowedCoresCount": 2,
   "minimumCoresCountPerInstance": 0
}

The meaning of the fields in the response:

Attribute	Explanation
`availableTextTypes`	Set of the text types available in the license
`availableBarcodeModules`	Set of the ABBYY FineReader Engine barcode modules available in the license
`availableEngineModules`	Set of the ABBYY FineReader Engine modules available in the license
`availableExportFormats`	Set of the export formats available in the license
`availableVisualComponents`	Set of visual components available in the license
`availableLanguageSets`	Set of the language sets available in the license
`volumeRefreshingPeriod`	Information about the limitation period if the license limits the number of processed pages or characters during this period
`volume`	Total number of pages or characters that can be processed during a period if the license has such a limitation
`volumeRemaining`	Remaining number of pages or characters that can be processed till the end of the current period if the license has such a limitation. When this property value reaches 0, analysis, recognition and export operations will not be possible.
`serialNumber`	Serial number of the license
`allowedCoresCount`	Number of CPU cores that can be used simultaneously. If the value of this property is 0, the number of CPU cores is unlimited.
`minimumCoresCountPerInstance`	Minimum number of CPU cores which is allocated by ABBYY FineReader Engine at initialization

GET /listLicenses

Returns a list of all licenses connected to the current ABBYY Engine.

List of licenses

[{
   "availableTextTypes":    [
      "ATT_Normal",
      "ATT_Typewriter",
      "ATT_Matrix",
      "ATT_Index",
      "ATT_OCR_A",
      "ATT_OCR_B",
      "ATT_MICR_E13B",
      "ATT_MICR_CMC7",
      "ATT_Advanced"
   ],
   "availableBarcodeModules":    [
      "ABM_1D",
      "ABM_PDF417",
      "ABM_Aztec",
      "ABM_QRCode",
      "ABM_MaxiCode",
      "ABM_DataMatrix",
      "ABM_Autolocation"
   ],
   "availableEngineModules":    [
      "AEM_ProcessAsPlainText",
      "AEM_Process",
      "AEM_Analyze",
      "AEM_Recognize",
      "AEM_Synthesize",
      "AEM_ExtendedCharacterInfo",
      "AEM_OpenPDF",
      "AEM_UserPatterns",
      "AEM_BalancedMode",
      "AEM_FastMode",
      "AEM_BCR",
      "AEM_Classification"
   ],
   "availableExportFormats":    [
      "AEF_RTF",
      "AEF_HTML",
      "AEF_XLS",
      "AEF_PDF",
      "AEF_Text",
      "AEF_PDFImageOnly",
      "AEF_XML",
      "AEF_PPT",
      "AEF_PDFA",
      "AEF_PDFMRC",
      "AEF_ALTO",
      "AEF_EPUB",
      "AEF_FB2",
      "AEF_ODT",
      "AEF_XPS"
   ],
   "availableVisualComponents": [],
   "availableLanguageSets":    [
      "ALS_Standard",
      "ALS_DataCapture",
      "ALS_Artificial",
      "ALS_Programming",
      "ALS_User",
      "ALS_Chinese",
      "ALS_Hebrew",
      "ALS_Thai",
      "ALS_Vietnamese",
      "ALS_Arabic",
      "ALS_Japanese",
      "ALS_Korean"
   ],
   "volumeRefreshingPeriod": "VRP_Infinite",
   "volume": 10000,
   "volumeRemaining": 10000,
   "serialNumber": "SWAT-1101-1004-1541-1060-5817",
   "allowedCoresCount": 2,
   "minimumCoresCountPerInstance": 0
}]

GET /api/v1/metrics/count

Request example:

The parameters are as follows:

status: either one of TaskStatus values or aggregate: DONE, PROCESSING, ALL
period: number of minutes to subtract from the current time. By default, the range is NOW-MINUTES to NOW. When the period is negative, the range is BEGINNING to NOW-MINUTES. The default is 60 minutes.

GET /api/v1/metrics/stats

Request example:

The parameters are as follows:

period: number of minutes to subtract from the current time. By default, the range is NOW-MINUTES to NOW. The default is 60 minutes.
minProcessingTime: tasks with processing time less than this value is discurded. The default is 1,000 (1 second).
stat: descriptive statistic: min, max, n (number of values), std, percentileN (where N is any number 0-100), or all.

Multi-worker processing

When process starting to work with specific task it changes task status, so other processes can't also start working with that task thus one task can be processed only by one process-worker.

When server during task processing is terminated we not able to send that task to the queue immediately, but we have service that runs on a schedule and puts such tasks to the queue.

Server specification and usage guidance

Currently, it's a low-parameters instance. Don't submit many documents. License allows only 10K pages to be recognized.

How to use OCR API

OCR is a technology for converting document images into editable text.

There is ability to recognize document by several ways:

via processImage API
via processDocument API

An example of picture recognition by several ways is provided below.

Recognition via processImage API

Authentication should be set if needed

POST host:port/api/v1/cloud/processImage

Start image processing with processImage.

Upload a document or an image as a file.
Add parameters for correct recognition.
Send a request.

A response has a lot of useful information: taskId, processing time, result Urls, status of recognition.

taskId: used during the next requests in the following steps (copy taskId from a response).
ResultUrl: a recognition result is available by this link after completion of recognition.

GET host:port/api/v1/cloud/getTaskStatus?taskId=value

Check task status with getTaskStatus (specify correct taskId).

The taskId value is determined in the previous step.

Set the taskId parameter and send a request.

As a result, the task status with resultUrls are provided in a response.

If the task is complete (status="Completed"), open results from the resultUrl, resultUrl2, or resultUrl3 fields (replace & with &).

GET host:port/api/v1/cloud/download?taskId=value

If a task is complete (status="Completed"), you can download results from links in resultUrl, resultUrl2, resultUrl3 (depends on the export format).

Recognition via processDocument API

POST host:port/api/v1/cloud/submitImage

Upload a file with submitImage, copy taskId from a response.

If you need to upload several files for recognition, do as follows:

Submit an image.
Copy ID.
Add the taskId parameter to submitImage API.
Submit one more image.

POST host:port/api/v1/cloud/processDocument

Start image processing with processDocument.

The taskId value is determined in the previous step. Set the taskId parameter.
Add parameters for correct recognition.
Send a request.

GET host:port/api/v1/cloud/getTaskStatus?taskId=value

Check a task status with getTaskStatus (specify correct taskId).

If a task is complete (status="Completed"), open results from the resultUrl, resultUrl2, or resultUrl3 fields (replace & with &).

GET host:port/api/v1/cloud/download?taskId=value

If a task is complete (status="Completed"), you can also download results from links in resultUrl, resultUrl2, resultUrl3 (depends on the export format).

OCR pattern creation

Extended recognition with a custom trained pattern can be used for:

Texts set in decorative fonts
Texts containing unusual characters, for example, mathematical symbols
Long documents of low print quality (more than a hundred pages)

For example:

The OCR product provides a possibility to create and train a user pattern that will be used for the further recognition.

The pattern training works as follows. Symbols are recognized in the training mode, and, subsequently, a pattern is created. The pattern is used as a source of additional information during recognition to aid recognition of the remaining text.

At first, prepare images and data for training. Then, prepare several examples of symbols in different views and define symbol parameters: baseLine, smallSymbolHeight.

baseLine contains the distance from the base line to the top edge of the cropped image of the character. The base line is the line on which the characters are located.

The top edge of the image is determined by the character orientation: H1 on the picture.

smallSymbolHeight specifies the height of small characters in pixels on the source image. H2 on the picture.

See the examples below:

Image	Value
	`baseLine=22` `smallSymbolHeight=14`
	`baseLine=19` `smallSymbolHeight=10`
	`baseLine=27` `smallSymbolHeight=18`
	`baseLine=34` `smallSymbolHeight=26`
	`baseLine=18` `smallSymbolHeight=12`
	`baseLine=28` `smallSymbolHeight=18`
	`baseLine=21` `smallSymbolHeight=14`
	`baseLine=19` `smallSymbolHeight=12`
	`baseLine=23` `smallSymbolHeight=15`
	`baseLine=22` `smallSymbolHeight=14`

You need to experiment with the baseLine and smallSymbolHeight parameters to optimize recognition.

To determine baseLine and smallSymbolHeight parameters, consider the symbol position on the text line within the actual document. For example, if you consider a checkbox in a tax form, it is much bigger than normal letters, and the base line is slightly higher (1 pixel for W-8BEN-E) than the bottom line of the checkbox. So don't guess the parameters looking at the symbol in isolation, consider the document layout.

Greater pictures of symbols are more efficient for training, preferably around 40-50 pixels.

Generally, you need multiple input images for training.

POST host:port/api/v1/cloud/trainPattern

Set all available examples of a symbol with parameters and required parameters to an API request.

file: image file with the symbol.
baseLine: value from the previous step.
smallSymbolHeight: value from the previous step.
symbol: symbol associated with pictures.

(symbol=₡)

Send and download the trainPattern result as a PTN file.

mergePattern

mergePattern: file upload for a pattern; if provided, the training output pattern is combined with the uploaded pattern.

Prepare images and data for training as was described in OCR pattern creation.

POST host:port/api/v1/cloud/trainPattern

Set all available examples of symbol with parameters and required parameters to an API request.

file: image file with the symbol
baseLine: value from the previous step
smallSymbolHeight: value from the previous step
symbol: symbol that is associated with pictures

(symbol=₡)

Use the mergePattern parameter to combine the existing pattern with trained in advanced.

In the current case, ae.ptn is a trained pattern for recognition of the Æ symbol.

Send and download the trainPattern result as a PTN file.

As a result, the trained pattern is available for recognition of two symbols.

Apply the pattern for the first symbol as described below.

pattern: file upload for a pattern to be applied to recognize special symbols.

note

You need the symbol to be added to alphabetExtension (alphabetExtension = Æ).
You can use either an explicit pattern uploaded with the field or useDefaultPattern=true, not both.

processImage: apply OCR pattern

POST host:port/api/v1/cloud/processImage

Add the trainPattern file to the ProcessImage request.

pattern: a file parameter for better recognition.

Download results where trainPattern is applied > symbol ₡ iss recognized as expected.

processDocument: apply OCR pattern

First, submit an image for recognition.

POST host:port/api/v1/cloud/submitImage

Copy ID for the subsequent steps.

After that, apply submitPattern trained and downloaded before.

POST host:port/api/v1/cloud/submitPattern

The taskId value is determined in the previous step.

Set the taskId parameter and a pattern file.

POST host:port/api/v1/cloud/processDocument

Start image processing with processDocument. The trained pattern was uploaded in the previous step.

Set the taskId parameter.
Add parameters for correct recognition.
Send a request.
Download results where trainPattern is applied > symbol ₡ was recognized as expected.

Practical usage tips

Recognition quality

Use custom dictionary.
Fetch plain text from pdf and set it to the originalText parameter.
Don't use the removeGarbageSize parameter for PDF searchable and carefully use it for scanned PDF or image. Sometimes, it can degrade results.
Train and use pattern for some repeatable unrecognized cases. By default, the costa_rica_currency pattern.
PDF searchable document is preferable to recognition than high DPI image created from it.
Recommended resolution for source image: 300 DPI for typical texts (10 pt or larger) and 400-600 DPI for texts in smaller fonts (9 pt or smaller).

Performance improvements

Use skipPreprocessing=true at least for PDF searchable documents. It saves up to 30% processing time
Use xml:writeRecognitionVariants=false if you don't need char or word variants in the output XML file. It saves up to 30% processing time, memory and disk space.