# Tess-TwoDemo **Repository Path**: ekber/Tess-TwoDemo ## Basic Information - **Project Name**: Tess-TwoDemo - **Description**: No description available - **Primary Language**: Unknown - **License**: Apache-2.0 - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2018-02-17 - **Last Updated**: 2020-12-18 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # Tess-TwoDemo tess-two是Tesseract在Android平台上的移植。 下载tess-two： ```groovy compile 'com.rmtheis:tess-two:8.0.0' ``` 然后将训练好的eng.traineddata放入android项目的assets文件夹中，就可以识别英文了。 # 1. 简单地识别英文 初始化tess-two，加载训练好的tessdata ```java private void prepareTesseract() { try { prepareDirectory(DATA_PATH + TESSDATA); } catch (Exception e) { e.printStackTrace(); } copyTessDataFiles(TESSDATA); } /** * Prepare directory on external storage * * @param path * @throws Exception */ private void prepareDirectory(String path) { File dir = new File(path); if (!dir.exists()) { if (!dir.mkdirs()) { Log.e(TAG, "ERROR: Creation of directory " + path + " failed, check does Android Manifest have permission to write to external storage."); } } else { Log.i(TAG, "Created directory " + path); } } /** * Copy tessdata files (located on assets/tessdata) to destination directory * * @param path - name of directory with .traineddata files */ private void copyTessDataFiles(String path) { try { String fileList[] = getAssets().list(path); for (String fileName : fileList) { // open file within the assets folder // if it is not already there copy it to the sdcard String pathToDataFile = DATA_PATH + path + "/" + fileName; if (!(new File(pathToDataFile)).exists()) { InputStream in = getAssets().open(path + "/" + fileName); OutputStream out = new FileOutputStream(pathToDataFile); // Transfer bytes from in to out byte[] buf = new byte[1024]; int len; while ((len = in.read(buf)) > 0) { out.write(buf, 0, len); } in.close(); out.close(); Log.d(TAG, "Copied " + fileName + "to tessdata"); } } } catch (IOException e) { Log.e(TAG, "Unable to copy files to tessdata " + e.toString()); } } ``` ![取景框.JPG](http://upload-images.jianshu.io/upload_images/2613397-201b49328b62f4e2.JPG?imageMogr2/auto-orient/strip%7CimageView2/2/w/400) 拍完照后，调用startOCR方法。 ```java private void startOCR(Uri imgUri) { try { BitmapFactory.Options options = new BitmapFactory.Options(); options.inSampleSize = 4; // 1 - means max size. 4 - means maxsize/4 size. Don't use value <4, because you need more memory in the heap to store your data. Bitmap bitmap = BitmapFactory.decodeFile(imgUri.getPath(), options); String result = extractText(bitmap); resultView.setText(result); } catch (Exception e) { Log.e(TAG, e.getMessage()); } } ``` extractText()会调用tess-two的api来实现ocr文字识别。 ```java private String extractText(Bitmap bitmap) { try { tessBaseApi = new TessBaseAPI(); } catch (Exception e) { Log.e(TAG, e.getMessage()); if (tessBaseApi == null) { Log.e(TAG, "TessBaseAPI is null. TessFactory not returning tess object."); } } tessBaseApi.init(DATA_PATH, lang); tessBaseApi.setImage(bitmap); String extractedText = "empty result"; try { extractedText = tessBaseApi.getUTF8Text(); } catch (Exception e) { Log.e(TAG, "Error in recognizing text."); } tessBaseApi.end(); return extractedText; } ``` 最后，显示识别的效果，此时的效果还算可以。 ![简单地识别英文.JPG](http://upload-images.jianshu.io/upload_images/2613397-26e4271a33dbb430.JPG?imageMogr2/auto-orient/strip%7CimageView2/2/w/800) # 2. 识别代码 接下来，尝试用上面的程序识别一段代码。 ![识别代码.JPG](http://upload-images.jianshu.io/upload_images/2613397-42e0a77fc16f9c7e.JPG?imageMogr2/auto-orient/strip%7CimageView2/2/w/800) 此时，效果一塌糊涂。我们重构一下startOCR()，增加局部的二值化处理。 ```java private void startOCR(Uri imgUri) { try { BitmapFactory.Options options = new BitmapFactory.Options(); options.inSampleSize = 4; // 1 - means max size. 4 - means maxsize/4 size. Don't use value <4, because you need more memory in the heap to store your data. Bitmap bitmap = BitmapFactory.decodeFile(imgUri.getPath(), options); CV4JImage cv4JImage = new CV4JImage(bitmap); Threshold threshold = new Threshold(); threshold.adaptiveThresh((ByteProcessor)(cv4JImage.convert2Gray().getProcessor()), Threshold.ADAPTIVE_C_MEANS_THRESH, 12, 30, Threshold.METHOD_THRESH_BINARY); Bitmap newBitmap = cv4JImage.getProcessor().getImage().toBitmap(Bitmap.Config.ARGB_8888); ivImage2.setImageBitmap(newBitmap); String result = extractText(newBitmap); resultView.setText(result); } catch (Exception e) { Log.e(TAG, e.getMessage()); } } ``` 在这里，使用cv4j来实现图像的二值化处理。 ```java CV4JImage cv4JImage = new CV4JImage(bitmap); Threshold threshold = new Threshold(); threshold.adaptiveThresh((ByteProcessor)(cv4JImage.convert2Gray().getProcessor()), Threshold.ADAPTIVE_C_MEANS_THRESH, 12, 30, Threshold.METHOD_THRESH_BINARY); Bitmap newBitmap = cv4JImage.getProcessor().getImage().toBitmap(Bitmap.Config.ARGB_8888); ``` >图像二值化就是将图像上的像素点的灰度值设置为0或255，也就是将整个图像呈现出明显的黑白效果。图像的二值化有利于图像的进一步处理，使图像变得简单，而且数据量减小，能凸显出感兴趣的目标的轮廓。 cv4j的github地址：https://github.com/imageprocessor/cv4j >[cv4j](https://github.com/imageprocessor/cv4j) 是[gloomyfish](http://blog.csdn.net/jia20003)和我一起开发的图像处理库，纯java实现。 再来试试效果，图片中间部分是二值化后的效果，此时基本能识别出代码的内容。 ![先做二值化再识别代码.JPG](http://upload-images.jianshu.io/upload_images/2613397-27d1cc9590b186c5.JPG?imageMogr2/auto-orient/strip%7CimageView2/2/w/800) # 3. 识别中文 如果要识别中文字体，需要使用中文的数据包。可以去下面的网站上下载。 https://github.com/tesseract-ocr/tessdata 跟中文相关的数据包有chi_sim.traineddata、chi_tra.traineddata，它们分别表示是简体中文和繁体中文。 ```java tessBaseApi.init(DATA_PATH, lang); ``` 前面的例子都是识别英文的，所以原先的lang值为"eng"，现在要识别简体中文的话需要将其值改为"chi_sim"。 ![识别中文.JPG](http://upload-images.jianshu.io/upload_images/2613397-93a085023a11c3e2.JPG?imageMogr2/auto-orient/strip%7CimageView2/2/w/800) # 最后 本项目只是demo级别的演示，离生产环境的使用还差的很远。 本项目的github地址：https://github.com/fengzhizi715/Tess-TwoDemo 为何说只是demo级别呢？ * 数据包很大，特别是中文的大概有50多M，放在移动端的肯定不合适。一般正确的做法，都是放在云端。 * 识别文字很慢，特别是中文，工程上还有很多优化的空间。 * 做ocr之前需要做很多预处理的工作，在本例子中只用了二值化，其实还有很多预处理的步骤比如倾斜校正、字符切割等等。 * 为了提高tess-two的识别率，可以自己训练数据集。