如何使用java从pdf文件获取原始文本?

I have some pdf files, Using pdfbox i have converted them into text and stored into text files, Now from the text files i want to remove

我有一些pdf文件，使用pdfbox我已经将它们转换成文本并存储到文本文件中，现在我想要删除的文本文件。

Hyperlinks
超链接
All special characters
所有的特殊字符
Blank lines
空白行
headers footers of pdf files
页眉页脚的pdf文件。
“1)”,“2)”, “a)”, “bullets”, etc.
“1)”、“(2)”、“)”,“子弹”,等等。

I want to get valid text line by line like this:

我想通过这样的方式得到有效的文本:

We propose OntoGain, a method for ontology learning from multi-word concept terms extracted from plain text. OntoGain follows an ontology learning process dened by distinct processing layers. Building upon plain term extraction a con-cept hierarchy is formed by clustering the extracted concepts. The derived term taxonomy is then enriched with non-taxonomic relations. Several dierent state-of-the-art methods have been examined for implementing each layer. OntoGain is based upon multi-word term concepts, as multi-word or compound terms are vested with more solid and distinctive semantics than plain single word terms. We opted for a hierarchical clustering method and Formal Concept Analysis (FCA) algorithm for building the term taxonomy. Furthermore an association rule algorithm is applied for revealing non-taxonomic relations. A method which tries to carry out the most appropriate generalization level between a relation's concepts is also implemented. To show proof of concept, a system prototype is implemented. The OntoGain allows transformation of the derived ontology into OWL using Jena Semantic Web Frame-work1. OntoGain is applied on two separate data sources a medical and computer corpus and its results are compared with similar results obtained by Text2Onto, a state-of-the-art-ontology learning method. The analysis of 11.5 CCD1.1 results indicates that OntoGain performs better than Text2Onto in terms of precision extracts more correct concepts while being more selective extracts fewer but more reasonable concepts.

本文提出了一种从纯文本中提取的多词概念术语的本体学习方法。OntoGain遵循了一个由不同的处理层所引发的本体学习过程。通过对提取出来的概念进行聚类，形成了一个简单的概念。然后，派生的术语分类法被非分类关系丰富。为了实现每一层，已经检查了几种最先进的方法。OntoGain是基于多词概念的，因为多词或复合词的语义比简单的单字术语更坚固和独特。我们选择了层次聚类方法和形式化概念分析(FCA)算法来构建术语分类。此外，还应用关联规则算法来揭示非分类关系。还实现了一种试图在关系的概念之间进行最适当的泛化级别的方法。为了证明概念的正确性，实现了一个系统原型。OntoGain允许通过Jena语义Web框架-work1将派生的本体转换为OWL。将OntoGain应用于两种不同的数据源，即医学和计算机语料库，并将其结果与text2on所得到的相似结果进行比较，这是一种基于情景的本体学习方法。对11.5 CCD1.1结果的分析表明，在精度提取上，OntoGain比text2on表现得更好，在更有选择性地提取更少但更合理的概念的同时，更准确地提取出更正确的概念。

How can I achieve this?

我怎样才能做到这一点呢?

4 个解决方案

#1

Using pdfbox we can achive this

使用pdfbox，我们可以做到这一点。

Example :

例子:

public static void main(String args[]) {

    PDFParser parser = null;
    PDDocument pdDoc = null;
    COSDocument cosDoc = null;
    PDFTextStripper pdfStripper;

    String parsedText;
    String fileName = "E:\\Files\\Small Files\\PDF\\JDBC.pdf";
    File file = new File(fileName);
    try {
        parser = new PDFParser(new FileInputStream(file));
        parser.parse();
        cosDoc = parser.getDocument();
        pdfStripper = new PDFTextStripper();
        pdDoc = new PDDocument(cosDoc);
        parsedText = pdfStripper.getText(pdDoc);
        System.out.println(parsedText.replaceAll("[^A-Za-z0-9. ]+", ""));
    } catch (Exception e) {
        e.printStackTrace();
        try {
            if (cosDoc != null)
                cosDoc.close();
            if (pdDoc != null)
                pdDoc.close();
        } catch (Exception e1) {
            e.printStackTrace();
        }

    }
}

#2

Hi we can extract the pdf files using Apache Tika

我们可以使用Apache Tika提取pdf文件。

The Example is :

的例子是:

import java.io.IOException;
import java.io.InputStream;
import java.util.HashMap;
import java.util.Map;
import org.apache.http.HttpEntity;
import org.apache.http.HttpResponse;
import org.apache.http.client.methods.HttpGet;
import org.apache.http.impl.client.DefaultHttpClient;
import org.apache.tika.metadata.Metadata;
import org.apache.tika.metadata.TikaCoreProperties;
import org.apache.tika.parser.AutoDetectParser;
import org.apache.tika.parser.ParseContext;
import org.apache.tika.sax.BodyContentHandler;

public class WebPagePdfExtractor {

    public Map<String, Object> processRecord(String url) {
        DefaultHttpClient httpclient = new DefaultHttpClient();
        Map<String, Object> map = new HashMap<String, Object>();
        try {
            HttpGet httpGet = new HttpGet(url);
            HttpResponse response = httpclient.execute(httpGet);
            HttpEntity entity = response.getEntity();
            InputStream input = null;
            if (entity != null) {
                try {
                    input = entity.getContent();
                    BodyContentHandler handler = new BodyContentHandler();
                    Metadata metadata = new Metadata();
                    AutoDetectParser parser = new AutoDetectParser();
                    ParseContext parseContext = new ParseContext();
                    parser.parse(input, handler, metadata, parseContext);
                    map.put("text", handler.toString().replaceAll("\n|\r|\t", " "));
                    map.put("title", metadata.get(TikaCoreProperties.TITLE));
                    map.put("pageCount", metadata.get("xmpTPg:NPages"));
                    map.put("status_code", response.getStatusLine().getStatusCode() + "");
                } catch (Exception e) {
                    e.printStackTrace();
                } finally {
                    if (input != null) {
                        try {
                            input.close();
                        } catch (IOException e) {
                            e.printStackTrace();
                        }
                    }
                }
            }
        } catch (Exception exception) {
            exception.printStackTrace();
        }
        return map;
    }

    public static void main(String arg[]) {
        WebPagePdfExtractor webPagePdfExtractor = new WebPagePdfExtractor();
        Map<String, Object> extractedMap = webPagePdfExtractor.processRecord("http://math.about.com/library/q20.pdf");
        System.out.println(extractedMap.get("text"));
    }

}

#3

You can use iText for do such things

你可以用iText做这样的事情。

//iText imports

import com.itextpdf.text.pdf.PdfReader;
import com.itextpdf.text.pdf.parser.PdfTextExtractor;

for example:

例如:

try {     
    PdfReader reader = new PdfReader(INPUTFILE);
    int n = reader.getNumberOfPages(); 
    String str=PdfTextExtractor.getTextFromPage(reader, 2); //Extracting the content from a particular page.
    System.out.println(str);
    reader.close();
} catch (Exception e) {
    System.out.println(e);
}

another one

另一个

try {

    PdfReader reader = new PdfReader("c:/temp/test.pdf");
    System.out.println("This PDF has "+reader.getNumberOfPages()+" pages.");
    String page = PdfTextExtractor.getTextFromPage(reader, 2);
    System.out.println("Page Content:\n\n"+page+"\n\n");
    System.out.println("Is this document tampered: "+reader.isTampered());
    System.out.println("Is this document encrypted: "+reader.isEncrypted());
} catch (IOException e) {
    e.printStackTrace();
}

the above examples can only extract the text, but you need to do some more to remove hyperlinks, bullets, heading & numbers.

上面的示例只能提取文本，但是您需要做更多的工作来删除超链接、子弹、标题和数字。

#4

Extracting all keywords from PDF file on your local machine or Base64 encoded string:

在本地机器或Base64编码的字符串中从PDF文件中提取所有关键字:

    import org.apache.commons.codec.binary.Base64;
    import org.apache.pdfbox.pdmodel.PDDocument;
    import org.apache.pdfbox.text.PDFTextStripper;

    import java.io.File;
    import java.io.FileInputStream;
    import java.io.FileNotFoundException;
    import java.io.IOException;
    import java.util.HashMap;
    import java.util.Map;

    public class WebPagePdfExtractor {

        public static void main(String arg[]) {
            WebPagePdfExtractor webPagePdfExtractor = new WebPagePdfExtractor();

            System.out.println("From file:   " + webPagePdfExtractor.processRecord(createByteArray()).get("text"));

            System.out.println("From string: " + webPagePdfExtractor.processRecord(getArrayFromBase64EncodedString()).get("text"));
        }

        public Map<String, Object> processRecord(byte[] byteArray) {
            Map<String, Object> map = new HashMap<>();
            try {
                PDFTextStripper stripper = new PDFTextStripper();
                stripper.setSortByPosition(false);
                stripper.setShouldSeparateByBeads(true);

                PDDocument document = PDDocument.load(byteArray);
                String text = stripper.getText(document);
                map.put("text", text.replaceAll("\n|\r|\t", " "));
            } catch (Exception exception) {
                exception.printStackTrace();
            }
            return map;
        }

        private static byte[] getArrayFromBase64EncodedString() {
            String encodedContent = "data:application/pdf;base64,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" +
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                    "uIAowMDAwMDAzNzk2IDAwMDAwIG4gCjAwMDAwMDg2ODcgMDAwMDAgbiAKMDAwMDAwODcwOCAwMDAwMCBuIAowMDAwMDA4NzI3IDAwMDAwIG4gCjAwMDAwMDg3ODAgMDAwMDAgbiAKMDAwMDAwODc5OSAwMDAwMCBuIAowMDAwMDA4ODE4IDAwMDAwIG4gCjAwMDAwMDg4NDUgMDAwMDAgbiAKMDAwMDAwODg4NyAwMDAwMCBuIAowMDAwMDA4OTA2IDAwMDAwIG4gCnRyYWlsZXIKPDwgL1NpemUgMjYgL1Jvb3QgMTQgMCBSIC9JbmZvIDEgMCBSIC9JRCBbIDxkYjc4M2NhNDM2Mzg4YzI5ZDc5MDQ2NzY3NjUxNjE3OT4KPGRiNzgzY2E0MzYzODhjMjlkNzkwNDY3Njc2NTE2MTc5PiBdID4+CnN0YXJ0eHJlZgo5MTA0CiUlRU9GCg==";
            String content = encodedContent.substring("data:application/pdf;base64," .length());
            return Base64.decodeBase64(content);
        }

        public static byte[] createByteArray() {
            String pathToBinaryData = "/bla-bla/src/main/resources/small.pdf";

            File file = new File(pathToBinaryData);
            if (!file.exists()) {
                System.out.println(" could not be found in folder " + pathToBinaryData);
                return null;
            }

            FileInputStream fin = null;
            try {
                fin = new FileInputStream(file);
            } catch (FileNotFoundException e) {
                e.printStackTrace();
            }

            byte fileContent[] = new byte[(int) file.length()];

            try {
                fin.read(fileContent);
            } catch (IOException e) {
                e.printStackTrace();
            }

            return fileContent;
        }
    }

#1