智能文本自动处理（Intelligent text automatic processing）(二)

2023-02-21 20:47 由石头木发表于 #其他

AutoText

智能文本自动处理工具（Intelligent text automatic processing tool）。

项目地址：https://github.com/jiangnanboy/AutoText

AutoText的功能主要有文本纠错，图片ocr以及表格结构识别等。

Guide

文本纠错
图片ocr
表格结构识别
Todo
Contact
Citation
License
Contribute

文本纠错

文本纠错部分详细见jcorrector
本项目目前主要包括：
- 基于ngram的纠错
- 基于深度学习的纠错
- 基于模板中文语法纠错
- 成语、专名纠错
具体使用见本项目中的examples/correct部分，或者jcorrector

图片ocr

这部分主要利用paddleocr 中的检测与识别部分，并将其中模型转为onnx格式进行调用，本项目在识别前对图片进行了预处理，使得在cpu环境下，平均一张图10秒左右。
具体使用见本项目中的examples/ocr/text/OcrDemo部分
PS
- 模型网盘下载
- 提取码：b5vq
- 模型下载后可放入resources的text_recgo下或其它位置
使用

    // read image file
    String imagePath = "examples\\ocr\\img_test\\text_example.png";
    var imageFile = Paths.get(imagePath);
    var image = ImageFactory.getInstance().fromFile(imageFile);
    
    // init model
    String detectionModelFile = OcrDemo.class.getClassLoader().getResource(PropertiesReader.get("text_recog_det_model_path")).getPath().replaceFirst("/", "");
    String recognitionModelFile = OcrDemo.class.getClassLoader().getResource(PropertiesReader.get("text_recog_rec_model_path")).getPath().replaceFirst("/", "");
    Path detectionModelPath = Paths.get(detectionModelFile);
    Path recognitionModelPath = Paths.get(recognitionModelFile);
    OcrApp ocrApp = new OcrApp(detectionModelPath, recognitionModelPath);
    ocrApp.init();
    
    // predict result and consume time
    var timeInferStart = System.currentTimeMillis();
    Pair<List<TextListBox>, Image> imagePair = ocrApp.ocrImage(image, 960);
    System.out.println("consume time: " + (System.currentTimeMillis() - timeInferStart)/1000.0 + "s");
    for (var result : imagePair.getLeft()) {
            System.out.println(result);
    }
    // save ocr result image
    ocrApp.saveImageOcrResult(imagePair, "ocr_result.png", "examples\\ocr\\output");
    ocrApp.closeAllModel();

结果，为文字及其坐标

    position: [800.0, 609.0, 877.0, 609.0, 877.0, 645.0, 800.0, 645.0], text: 8.23%
    position: [433.0, 607.0, 494.0, 607.0, 494.0, 649.0, 433.0, 649.0], text: 68.4
    position: [96.0, 610.0, 316.0, 611.0, 316.0, 641.0, 96.0, 640.0], text: 股东权益比率（%）
    position: [624.0, 605.0, 688.0, 605.0, 688.0, 650.0, 624.0, 650.0], text: 63.2
    position: [791.0, 570.0, 887.0, 570.0, 887.0, 600.0, 791.0, 600.0], text: -39.64%
    position: [625.0, 564.0, 687.0, 564.0, 687.0, 606.0, 625.0, 606.0], text: 49.7
    position: [134.0, 568.0, 279.0, 568.0, 279.0, 598.0, 134.0, 598.0], text: 毛利率（%）
    ......

结果图片展示图片OCR

表格结构识别

基于规则由opencv研发，主要识别的表格类型有：有边界表格、无边界表格以及部分有边界表格。
具体使用见本项目中的examples/ocr/table/TableDemo部分
使用

    public static void borderedRecog() {
            String imagePath = "D:\\project\\idea_workspace\\AutoText\\src\\main\\java\\examples\\ocr\\img_test\\bordered_example.png";
            Mat imageMat = imread(imagePath);
            System.out.println("imageMat : " + imageMat.size().height() + " " + imageMat.size().width() + " ");
            List<List<List<Integer>>> resultList = BorderedRecog.recognizeStructure(imageMat);
            System.out.println(resultList);
    //        ImageUtils.imshow("Image", pair.getRight());
        }
    
    public static void unBorderedRecog() {
        String imagePath = "D:\\project\\idea_workspace\\AutoText\\src\\main\\java\\examples\\ocr\\img_test\\unbordered_example.jpg";
        Mat imageMat = imread(imagePath);
        System.out.println("imageMat : " + imageMat.size().height() + " " + imageMat.size().width() + " ");
        List<List<List<Integer>>> resultList = UnBorderedRecog.recognizeStructure(imageMat);
        System.out.println(resultList);
//        ImageUtils.imshow("Image", pair.getRight());
    }

    public static void partiallyBorderedRecog() {
        String imagePath = "D:\\project\\idea_workspace\\AutoText\\src\\main\\java\\examples\\ocr\\img_test\\partially_example.jpg";
        Mat imageMat = imread(imagePath);
        System.out.println("imageMat : " + imageMat.size().height() + " " + imageMat.size().width() + " ");
        List<List<List<Integer>>> resultList = PartiallyBorderedRecog.recognizeStructure(imageMat);
        System.out.println(resultList);
//        ImageUtils.imshow("Image", pair.getRight());
    }

结果，为表格单元格坐标

    [[[58, 48, 247, 182], [560, 48, 247, 182], [811, 48, 246, 182], [309, 48, 247, 182], [1312, 48, 247, 182], 
    [1061, 48, 247, 182]], [[58, 234, 247, 118], [309, 234, 247, 118], [1061, 234, 247, 118], [560, 234, 247, 118], 
    [811, 234, 246, 118], [1312, 234, 247, 118]], [[58, 356, 247, 118], [309, 356, 247, 118], [560, 356, 247, 118], 
    [811, 356, 246, 118], [1061, 356, 247, 118], [1312, 356, 247, 118]], [[58, 478, 247, 118], [309, 478, 247, 118],
    [560, 478, 247, 118], [811, 478, 246, 118], [1061, 478, 247, 118], [1312, 478, 247, 118]], [[58, 600, 247, 119],
    [309, 600, 247, 119], [560, 600, 247, 119], [811, 600, 246, 119], [1061, 600, 247, 119], [1312, 600, 247, 119]], 
    [[58, 723, 247, 118], [309, 723, 247, 118], [560, 723, 247, 118], [1061, 723, 247, 118], [1312, 723, 247, 118], 
    [811, 723, 246, 118]], [[58, 845, 247, 118], [309, 845, 247, 118], [560, 845, 247, 118], [811, 845, 246, 118], 
    [1312, 845, 247, 118], [1061, 845, 247, 118]]]

结果图片展示表格结构

表格结构和OCR

这部分将整合表格结构和OCR识别，同时识别出表格单元格和OCR文本。
具体使用见本项目中的examples/ocr/table_text/TableTextDemo部分
使用

    public static void main(String...args) throws IOException, TranslateException {
            String imagePath = "D:\\project\\idea_workspace\\AutoText\\src\\main\\java\\examples\\ocr\\img_test\\bordered_example.png";
            TableText tableText = new TableText();
            /**
             * maxSideLen:image resize
             *
             * borderType:{0:all, 1:bordered(default), 2:unbordered, 3:partiallybordered}
             */
            int maxSideLen = -1; // default, no resize
            int borderType = 1; // default, bordered
            List<TextListBox> listBoxes = tableText.tableTextRecog(imagePath);
            for(TextListBox textListBox : listBoxes) {
                System.out.print(textListBox);
            }
        }

结果，为表格单元格坐标以及单元格内的文本

    position: [58.0, 48.0, 305.0, 48.0, 305.0, 230.0, 58.0, 230.0], text: 节次 星期
    position: [309.0, 48.0, 556.0, 48.0, 556.0, 230.0, 309.0, 230.0], text: 周一
    position: [811.0, 48.0, 1057.0, 48.0, 1057.0, 230.0, 811.0, 230.0], text: 周三
    position: [1061.0, 48.0, 1308.0, 48.0, 1308.0, 230.0, 1061.0, 230.0], text: 周四
    position: [560.0, 48.0, 807.0, 48.0, 807.0, 230.0, 560.0, 230.0], text: 周二
    position: [1312.0, 48.0, 1559.0, 48.0, 1559.0, 230.0, 1312.0, 230.0], text: 周五
    position: [58.0, 234.0, 305.0, 234.0, 305.0, 352.0, 58.0, 352.0], text: 
    position: [309.0, 234.0, 556.0, 234.0, 556.0, 352.0, 309.0, 352.0], text: 语文
    position: [811.0, 234.0, 1057.0, 234.0, 1057.0, 352.0, 811.0, 352.0], text: 英语
    position: [560.0, 234.0, 807.0, 234.0, 807.0, 352.0, 560.0, 352.0], text: 英语
    position: [1061.0, 234.0, 1308.0, 234.0, 1308.0, 352.0, 1061.0, 352.0], text: 自然
    position: [1312.0, 234.0, 1559.0, 234.0, 1559.0, 352.0, 1312.0, 352.0], text: 数学
    position: [58.0, 356.0, 305.0, 356.0, 305.0, 474.0, 58.0, 474.0], text: 3
    position: [560.0, 356.0, 807.0, 356.0, 807.0, 474.0, 560.0, 474.0], text: 英语
    position: [309.0, 356.0, 556.0, 356.0, 556.0, 474.0, 309.0, 474.0], text: 语文
    position: [811.0, 356.0, 1057.0, 356.0, 1057.0, 474.0, 811.0, 474.0], text: 英语
    position: [1312.0, 356.0, 1559.0, 356.0, 1559.0, 474.0, 1312.0, 474.0], text: 数学
    position: [1061.0, 356.0, 1308.0, 356.0, 1308.0, 474.0, 1061.0, 474.0], text: 语文
    position: [58.0, 478.0, 305.0, 478.0, 305.0, 596.0, 58.0, 596.0], text: 三
    position: [309.0, 478.0, 556.0, 478.0, 556.0, 596.0, 309.0, 596.0], text: 数学
    position: [560.0, 478.0, 807.0, 478.0, 807.0, 596.0, 560.0, 596.0], text: 语文
    position: [811.0, 478.0, 1057.0, 478.0, 1057.0, 596.0, 811.0, 596.0], text: 数学
    position: [1312.0, 478.0, 1559.0, 478.0, 1559.0, 596.0, 1312.0, 596.0], text: 英语
    position: [1061.0, 478.0, 1308.0, 478.0, 1308.0, 596.0, 1061.0, 596.0], text: 语文
    position: [58.0, 600.0, 305.0, 600.0, 305.0, 719.0, 58.0, 719.0], text: 四
    position: [309.0, 600.0, 556.0, 600.0, 556.0, 719.0, 309.0, 719.0], text: 数学
    position: [811.0, 600.0, 1057.0, 600.0, 1057.0, 719.0, 811.0, 719.0], text: 数学
    position: [560.0, 600.0, 807.0, 600.0, 807.0, 719.0, 560.0, 719.0], text: 语文
    position: [1061.0, 600.0, 1308.0, 600.0, 1308.0, 719.0, 1061.0, 719.0], text: 体育
    position: [1312.0, 600.0, 1559.0, 600.0, 1559.0, 719.0, 1312.0, 719.0], text: 英语
    position: [58.0, 723.0, 305.0, 723.0, 305.0, 841.0, 58.0, 841.0], text: 五
    position: [560.0, 723.0, 807.0, 723.0, 807.0, 841.0, 560.0, 841.0], text: 思想品德
    position: [309.0, 723.0, 556.0, 723.0, 556.0, 841.0, 309.0, 841.0], text: 体育
    position: [1061.0, 723.0, 1308.0, 723.0, 1308.0, 841.0, 1061.0, 841.0], text: 数学
    position: [1312.0, 723.0, 1559.0, 723.0, 1559.0, 841.0, 1312.0, 841.0], text: 手工
    position: [811.0, 723.0, 1057.0, 723.0, 1057.0, 841.0, 811.0, 841.0], text: 语文
    position: [58.0, 845.0, 305.0, 845.0, 305.0, 963.0, 58.0, 963.0], text: 六
    position: [309.0, 845.0, 556.0, 845.0, 556.0, 963.0, 309.0, 963.0], text: 美术
    position: [560.0, 845.0, 807.0, 845.0, 807.0, 963.0, 560.0, 963.0], text: 音乐
    position: [1061.0, 845.0, 1308.0, 845.0, 1308.0, 963.0, 1061.0, 963.0], text: 数学
    position: [811.0, 845.0, 1057.0, 845.0, 1057.0, 963.0, 811.0, 963.0], text: 语文
    position: [1312.0, 845.0, 1559.0, 845.0, 1559.0, 963.0, 1312.0, 963.0], text: 写字

Todo

加入jcorrector文本纠错，修改部分程序
基于paddleocr模型，利用java实现图片ocr
基于规则利用opencv识别表格结构
整合规则表格识别与OCR识别

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