是否有一个OCR库可以输出图像中找到的单词坐标？

时间：2011-02-18 12:01:55

标签： ocr

根据我的经验，OCR库往往只输出在图像中找到的文本，而不是 找到文本。是否有一个OCR库可以输出图像中找到的单词以及找到这些单词的坐标（x, y, width, height）？

9 个答案:

答案 0 :(得分：22)

大多数商业OCR引擎都会返回单词和字符坐标位置，但您必须使用其SDK来提取信息。即使是Tesseract OCR也会返回位置信息，但这并不容易。版本3.01将变得更容易，但仍在处理DLL接口。

不幸的是，大多数免费的OCR程序以其基本形式使用Tesseract OCR，它们只报告原始ASCII结果。

www.transym.com - Transym OCR - 输出坐标。 www.rerecognition.com - Kasmos引擎返回坐标。

Caere Omnipage，Mitek，Abbyy，Charactell也回归角色。

答案 1 :(得分：14)

我正在使用TessNet（Tesseract C＃包装器），我正在使用以下代码获取单词坐标：

TextWriter tw = new StreamWriter(@"U:\user files\bwalker\ocrTesting.txt");
Bitmap image = new Bitmap(@"u:\user files\bwalker\2849257.tif");
tessnet2.Tesseract ocr = new tessnet2.Tesseract();
// If digit only
ocr.SetVariable("tessedit_char_whitelist", "0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz.,$-/#&=()\"':?");
// To use correct tessdata
ocr.Init(@"C:\Users\bwalker\Documents\Visual Studio 2010\Projects\tessnetWinForms\tessnetWinForms\bin\Release\", "eng", false); 
List<tessnet2.Word> result = ocr.DoOCR(image, System.Drawing.Rectangle.Empty);
string Results = "";
foreach (tessnet2.Word word in result)
{
    Results += word.Confidence + ", " + word.Text + ", " +word.Top+", "+word.Bottom+", "+word.Left+", "+word.Right+"\n";
}
using (StreamWriter writer = new StreamWriter(@"U:\user files\bwalker\ocrTesting2.txt", true))
{
    writer.WriteLine(Results);//+", "+word.Top+", "+word.Bottom+", "+word.Left+", "+word.Right);
    writer.Close();
}
MessageBox.Show("Completed");

答案 2 :(得分：2)

您还可以查看Gamera框架（http://gamera.informatik.hsnr.de/）它是一组工具，可以让您构建自己的OCR引擎。然而，最快的方法是使用Tesseract或OCRopus hOCR（http://en.wikipedia.org/wiki/HOCR）输出。

答案 3 :(得分：2)

您可以像tesseract那样使用hocr“configfile”：

tesseract syllabus-page1.jpg syllabus-page1 hocr

这将输出一个主要是HTML5的文档，其中包含以下元素：

<div class='ocr_page' id='page_1' title='image "syllabus-page1.jpg"; bbox 0 0 2531 3272; ppageno 0'>
  <div class="ocr_carea" id="block_1_4" title="bbox 265 1183 2147 1778">
    <p class="ocr_par" dir="ltr" id="par_1_8" title="bbox 274 1305 655 1342">
      <span class="ocr_line" id="line_1_14" title="bbox 274 1305 655 1342; baseline -0.005 0; x_size 46.378059; x_descenders 10.378059; x_ascenders 12">
        <span class="ocrx_word" id="word_1_78" title="bbox 274 1307 386 1342; x_wconf 90" lang="eng" dir="ltr">needs</span>
        <span class="ocrx_word" id="word_1_79" title="bbox 402 1318 459 1342; x_wconf 90" lang="eng" dir="ltr">are</span>
        <span class="ocrx_word" id="word_1_80" title="bbox 474 1305 655 1341; x_wconf 86" lang="eng" dir="ltr">different:</span>
      </span>
    </p>
    ...
  </div>  
  ...
</div>

虽然我很确定你不应该如何使用XML，但我发现它比挖掘tesseract API更容易。

P.S。我意识到有几条评论和答案提到了这个解决方案，但它们都没有实际展示如何使用hocr选项或描述你从中获得的输出。

答案 4 :(得分：1)

对于Java开发人员：

我建议您使用Tesseract和Tess4j。

你可以在Tess4j的一个测试中找到一个如何在Image上查找单词的例子。

https://github.com/nguyenq/tess4j/blob/master/src/test/java/net/sourceforge/tess4j/TessAPITest.java#L449-L517 

public void testResultIterator() throws Exception {
    logger.info("TessBaseAPIGetIterator");
    File tiff = new File(this.testResourcesDataPath, "eurotext.tif");
    BufferedImage image = ImageIO.read(new FileInputStream(tiff)); // require jai-imageio lib to read TIFF
    ByteBuffer buf = ImageIOHelper.convertImageData(image);
    int bpp = image.getColorModel().getPixelSize();
    int bytespp = bpp / 8;
    int bytespl = (int) Math.ceil(image.getWidth() * bpp / 8.0);
    api.TessBaseAPIInit3(handle, datapath, language);
    api.TessBaseAPISetPageSegMode(handle, TessPageSegMode.PSM_AUTO);
    api.TessBaseAPISetImage(handle, buf, image.getWidth(), image.getHeight(), bytespp, bytespl);
    ETEXT_DESC monitor = new ETEXT_DESC();
    TimeVal timeout = new TimeVal();
    timeout.tv_sec = new NativeLong(0L); // time > 0 causes blank ouput
    monitor.end_time = timeout;
    ProgressMonitor pmo = new ProgressMonitor(monitor);
    pmo.start();
    api.TessBaseAPIRecognize(handle, monitor);
    logger.info("Message: " + pmo.getMessage());
    TessResultIterator ri = api.TessBaseAPIGetIterator(handle);
    TessPageIterator pi = api.TessResultIteratorGetPageIterator(ri);
    api.TessPageIteratorBegin(pi);
    logger.info("Bounding boxes:\nchar(s) left top right bottom confidence font-attributes");
    int level = TessPageIteratorLevel.RIL_WORD;

    // int height = image.getHeight();
    do {
        Pointer ptr = api.TessResultIteratorGetUTF8Text(ri, level);
        String word = ptr.getString(0);
        api.TessDeleteText(ptr);
        float confidence = api.TessResultIteratorConfidence(ri, level);
        IntBuffer leftB = IntBuffer.allocate(1);
        IntBuffer topB = IntBuffer.allocate(1);
        IntBuffer rightB = IntBuffer.allocate(1);
        IntBuffer bottomB = IntBuffer.allocate(1);
        api.TessPageIteratorBoundingBox(pi, level, leftB, topB, rightB, bottomB);
        int left = leftB.get();
        int top = topB.get();
        int right = rightB.get();
        int bottom = bottomB.get();
        /******************************************/
        /* COORDINATES AND WORDS ARE PRINTED HERE */
        /******************************************/
        System.out.print(String.format("%s %d %d %d %d %f", word, left, top, right, bottom, confidence));
        // logger.info(String.format("%s %d %d %d %d", str, left, height - bottom, right, height - top)); //
        // training box coordinates

        IntBuffer boldB = IntBuffer.allocate(1);
        IntBuffer italicB = IntBuffer.allocate(1);
        IntBuffer underlinedB = IntBuffer.allocate(1);
        IntBuffer monospaceB = IntBuffer.allocate(1);
        IntBuffer serifB = IntBuffer.allocate(1);
        IntBuffer smallcapsB = IntBuffer.allocate(1);
        IntBuffer pointSizeB = IntBuffer.allocate(1);
        IntBuffer fontIdB = IntBuffer.allocate(1);
        String fontName = api.TessResultIteratorWordFontAttributes(ri, boldB, italicB, underlinedB, monospaceB,
                serifB, smallcapsB, pointSizeB, fontIdB);
        boolean bold = boldB.get() == TRUE;
        boolean italic = italicB.get() == TRUE;
        boolean underlined = underlinedB.get() == TRUE;
        boolean monospace = monospaceB.get() == TRUE;
        boolean serif = serifB.get() == TRUE;
        boolean smallcaps = smallcapsB.get() == TRUE;
        int pointSize = pointSizeB.get();
        int fontId = fontIdB.get();
        logger.info(String.format("  font: %s, size: %d, font id: %d, bold: %b,"
                + " italic: %b, underlined: %b, monospace: %b, serif: %b, smallcap: %b", fontName, pointSize,
                fontId, bold, italic, underlined, monospace, serif, smallcaps));
    } while (api.TessPageIteratorNext(pi, level) == TRUE);

    assertTrue(true);
}

答案 5 :(得分：1)

Google Vision API会这样做。 https://cloud.google.com/vision/docs/detecting-text 

"description": "Wake up human!\n",
      "boundingPoly": {
        "vertices": [
          {
            "x": 29,
            "y": 394
          },
          {
            "x": 570,
            "y": 394
          },
          {
            "x": 570,
            "y": 466
          },
          {
            "x": 29,
            "y": 466
          }
        ]
      }

答案 6 :(得分：0)

ABCocr.NET（我们的组件）将允许您获取找到的每个单词的坐标。可以通过Word.Bounds属性访问这些值，该属性只返回System.Drawing.Rectangle。

以下示例显示了如何使用ABCocr.NET对图像进行OCR并输出所需信息：

using System;
using System.Drawing;
using WebSupergoo.ABCocr3;

namespace abcocr {
    class Program {
        static void Main(string[] args) {

            Bitmap bitmap = (Bitmap)Bitmap.FromFile("example.png");
            Ocr ocr = new Ocr();
            ocr.SetBitmap(bitmap);

            foreach (Word word in ocr.Page.Words) {
                Console.WriteLine("{0}, X: {1}, Y: {2}, Width: {3}, Height: {4}",
                    word.Text,
                    word.Bounds.X,
                    word.Bounds.Y,
                    word.Bounds.Width,
                    word.Bounds.Height);
            }
        }
    }
}

披露：由WebSupergoo团队成员发布。

答案 7 :(得分：0)

hocr是tesseract OCR引擎的输出格式之一，它具有单词和它的坐标，还有一些额外的信息，如自信的单词识别级别。

答案 8 :(得分：0)

设置参数isOverlayRequired = true后，免费的OCR.space OCR API会返回单词坐标：

 "ParsedResults" : [
            {
                "TextOverlay" : {
                    "Lines" : [
                        {
                            "Words": [
                                {
                                "WordText": "Word 1",
                                "Left": 106,
                                "Top": 91,
                                "Height": 9,
                                "Width": 11
                                },
                                {
                                "WordText": "Word 2",
                                "Left": 121,
                                "Top": 90,
                                "Height": 13,
                                "Width": 51
                                }
                                .
相关问题
最新问题