根据IDC的报道，超过80%的信息是基于NoSQL的，尤其是文本文件。当数字服务或应用程序不能处理所有这些信息时，企业就会遭受损失。为了面对这一挑战，可以使用OCR技术。OCR使用机器学习和/或训练的图像模式将图像像素转化为文本。这一点很重要，因为许多文件被扫描成PDF格式的图像，或者许多文件中包含有文本的图像。因此，OCR是一个重要的步骤，可以从文件中获得所有可能的数据。
为了实现OCR，可以使用开源解决方案Google Tesseract，这是Python和Java社区中最流行的解决方案。Tesseract支持100多个习语，并且可以用新的模型进行训练以识别车牌、验证码等等。Tesseract是在C++中创建的，可以通过Java套用Tess4J构成一个中介层来使用它。下面的代码展示了调用过程。
private String extractTextFromImage ( File tempFile ) throws TesseractException {
ITesseract tesseract = new Tesseract ();
tesseract . setDatapath ( "/usr/share/tessdata/" ); //directory to trained models
tesseract . setLanguage ( "eng+por" ); // choose your language/trained model
return tesseract . doOCR (tempFile); //call tesseract function doOCR()
//passing the file to be processed with OCR technique
}
为了让IRIS使用这个Java类并从Java获得结果，我们需要使用PEX和Java网关解决方案。
首先，有必要在Production中配置Java代理，其次，配置一个PEX业务操作或服务来在Production中连接沟通IRIS和Java。
Class dc . ocr . OcrProduction Extends Ens .Production
{
XData ProductionDefinition
{
< Production Name = "dc.ocr.OcrProduction" LogGeneralTraceEvents = "false" >
< Description ></ Description >
< ActorPoolSize > 2 </ ActorPoolSize >
< Item Name = "OcrService" Category = "" ClassName = "dc.ocr.OcrService" PoolSize = "1" Enabled = "true"
Foreground = "false" Comment = "" LogTraceEvents = "false" Schedule = "" >
</ Item >
< Item Name = "JavaGateway" Category = "" ClassName = "EnsLib.JavaGateway.Service" PoolSize = "1"
Enabled = "true" Foreground = "false" Comment = "" LogTraceEvents = "false" Schedule = "" >
< Setting Target = "Host" Name = "ClassPath" > .:/usr/irissys/dev/java/lib/JDK18/*:/opt/irisapp/*
:/usr/irissys/dev/java/lib/gson/*
:/usr/irissys/dev/java/lib/jackson/*:/jgw/ocr-pex-1.0.0.jar
</ Setting >
< Setting Target = "Host" Name = "JavaHome" > /usr/lib/jvm/java-8-openjdk-amd64/ </ Setting >
</ Item >
< Item Name = "OcrOperation" Category = "" ClassName = "EnsLib.PEX.BusinessOperation" PoolSize = "1"
Enabled = "true" Foreground = "false" Comment = "" LogTraceEvents = "false" Schedule = "" >
< Setting Target = "Host" Name = "%gatewayPort" > 55555 </ Setting >
< Setting Target = "Host" Name = "%remoteClassname" > community.intersystems.pex.ocr.OcrOperation </ Setting >
< Setting Target = "Host" Name = "%gatewayExtraClasspaths" > .:/usr/irissys/dev/java/lib/JDK18/*
:/opt/irisapp/*:/usr/irissys/dev/java/lib/gson/*
:/usr/irissys/dev/java/lib/jackson/*
:/jgw/ocr-pex-1.0.0.jar
</ Setting >
</ Item >
</ Production >
}
}
现在，任何IRIS Production都可以与Java和Tesseract进行通信了! 如下：
//call ocr method to get text from image, if you want to use pex
Set pRequest = ##class ( dc . ocr . OcrRequest ). %New ()
Set pRequest . FileName = file . Filename
// call java pex operation to do ocr, passing file into pRequest and receive ocr text with pResponse
Set tSC = .. SendRequestSync ( "OcrOperation" , pRequest , . pResponse , 1200 )
//save the results into database to use text analytics - nlp
Set ocrTable = ##class ( dc . ocr . OcrTable ). %New ()
Set ocrTable . FileName = file . Filename
Set ocrTable . OcrText = pResponse . StringValue
Set tSC = ocrTable . %Save ()
所有的代码细节，连同注释都可以在我的OCR服务库(https://openexchange.intersystems.com/package/OCR-Service)中找到。
现在，随着文本的提取，我们需要使用IRIS NLP引擎来分析文本数据，并获得支持决策的见解。为此，当文本被提取后，它被保存到一个表中，这个表被NLP引擎用作文本源。请看上面的表%Save()，请看下面的代码，NLP引用OCRTable(有文本提取的地方)。如下：
Class dc . ocr . OcrNLP Extends %iKnow .DomainDefinition [ ProcedureBlock ]
{
XData Domain [ XMLNamespace = "http://www.intersystems.com/iknow" ]
{
< domain name = "OcrNLP" disabled = "false" allowCustomUpdates = "true" >
< parameter name = "DefaultConfig" value = "OcrNLP.Configuration" isList = "false" />
< data dropBeforeBuild = "true" >
< table listname = "OcrNLPTable" batchMode = "true" disabled = "false"
listerClass = "%iKnow.Source.SQL.Lister" tableName = "dc_ocr.OcrTable" idField = "ID"
groupField = "ID" dataFields = "OcrText" metadataColumns = "FileName" metadataFields = "filename" />
</ data >
< matching disabled = "false" dropBeforeBuild = "true" autoExecute = "true" ignoreDictionaryErrors = "true" />
< metadata >
< field name = "filename" operators = "=" dataType = "STRING" storage = "0" caseSensitive = "false" disabled = "false" />
</ metadata >
< configuration name = "OcrNLP.Configuration" detectLanguage = "true" languages = "en,pt"
userDictionary = "OcrNLP.Dictionary#1" summarize = "true" maxConceptLength = "0" />
< userDictionary name = "OcrNLP.Dictionary#1" />
</ domain >
}
}
在我的OCR服务github资源库中看到完整的细节和配置。
现在我们可以上传一些文件，到资源管理器中查看概念和生成的CRC。
请参阅动画与这里讨论的所有步骤。
欢迎尝试 OCR/NLP!
