【Keras+计算机视觉+Tensorflow】OCR文字识别实战（附源码和数据集 超详细必看）(计算机视觉:一种现代方法)

【Keras+计算机视觉+Tensorflow】OCR文字识别实战（附源码和数据集 超详细必看）

推荐整理分享【Keras+计算机视觉+Tensorflow】OCR文字识别实战（附源码和数据集 超详细必看）(计算机视觉:一种现代方法)，希望有所帮助，仅作参考，欢迎阅读内容。

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需要源码和数据集请点赞关注收藏后评论区留言私信~~~

一、OCR文字识别简介

利用计算机自动识别字符的技术，是模式识别应用的一个重要领域。人们在生产和生活中，要处理大量的文字、报表和文本。为了减轻人们的劳动，提高处理效率，从上世纪50年代起就开始探讨文字识别方法，并研制出光学字符识别器。

OCR(Optical Character Recognition)图像文字识别是人工智能的重要分支，赋予计算机人眼的功能，使其可以看图识字，图像文字识别系统流程一般分为图像采集、文字检测、文字识别以及结果输出四部分。

 二、OCR文字识别项目实战1：数据集简介

MSRA-TD500该数据集共包含500 张自然场景图像，其分辨率在1296 ´ 864至920 ´ 1280 之间，涵盖了室内商场、标识牌、室外街道、广告牌等大多数场，文本包含中文和英文，有着不同的字体、大小和倾斜方向，部分数据集图像如下图所示。

 数据集项目结构如下 分为训练集和测试集

2：项目结构

整体项目结构如下 上面是一些算法和模型比如CRAFT CRNN的定义，下面是测试代码

 CRAFT算法实现文本行的检测如图下图所示。首先将完整的文字区域输入CRAFT文字检测网络，得到字符级的文字得分结果热图（Text Score）和字符级文本连接得分热图（Link Score），最后根据连通域得到每个文本行的位置

3：效果展示 

开始运行代码

输出运行结果 可以放入不同图片进行测试 

 

 

 

 

三、代码 

部分代码如下 需要全部代码和数据集请点赞关注收藏后评论区留言私信~~~  

"""This script demonstrates how to train the modelon the SynthText90 using multiple GPUs."""# pylint: disable=invalid-nameimport datetimeimport argparseimport mathimport randomimport stringimport functoolsimport itertoolsimport osimport tarfileimport urllib.requestimport numpy as npimport cv2import imgaugimport tqdmimport tensorflow as tfimport keras_ocr# pylint: disable=redefined-outer-namedef get_filepaths(data_path, split): """Get the list of filepaths for a given split (train, val, or test).""" with open(os.path.join(data_path, f'mnt/ramdisk/max/90kDICT32px/annotation_{split}.txt'), 'r') as text_file: filepaths = [ os.path.join(data_path, 'mnt/ramdisk/max/90kDICT32px', line.split(' ')[0][2:]) for line in text_file.readlines() ] return filepaths# pylint: disable=redefined-outer-namedef download_extract_and_process_dataset(data_path): """Download and extract the synthtext90 dataset.""" archive_filepath = os.path.join(data_path, 'mjsynth.tar.gz') extraction_directory = os.path.join(data_path, 'mnt') if not os.path.isfile(archive_filepath) and not os.path.isdir(extraction_directory): print('Downloading the dataset.') urllib.request.urlretrieve("https://www.robots.ox.ac.uk/~vgg/data/text/mjsynth.tar.gz", archive_filepath) if not os.path.isdir(extraction_directory): print('Extracting files.') with tarfile.open(os.path.join(data_path, 'mjsynth.tar.gz')) as tfile: tfile.extractall(data_path)def get_image_generator(filepaths, augmenter, width, height): """Get an image generator for a list of SynthText90 filepaths.""" filepaths = filepaths.copy() for filepath in itertools.cycle(filepaths): text = filepath.split(os.sep)[-1].split('_')[1].lower() image = cv2.imread(filepath) if image is None: print(f'An error occurred reading: {filepath}') image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) image = keras_ocr.tools.fit(image, width=width, height=height, cval=np.random.randint(low=0, high=255, size=3).astype('uint8')) if augmenter is not None: image = augmenter.augment_image(image) if filepath == filepaths[-1]: random.shuffle(filepaths) yield image, textif __name__ == '__main__': parser = argparse.ArgumentParser(description='Process some integers.') parser.add_argument('--model_id', default='recognizer', help='The name to use for saving model checkpoints.') parser.add_argument( '--data_path', default='.', help='The path to the directory containing the dataset and where we will put our logs.') parser.add_argument( '--logs_path', default='./logs', help=( 'The path to where logs and checkpoints should be stored. ' 'If a checkpoint matching "model_id" is found, training will resume from that point.')) parser.add_argument('--batch_size', default=16, help='The training batch size to use.') parser.add_argument('--no-file-verification', dest='verify_files', action='store_false') parser.set_defaults(verify_files=True) args = parser.parse_args() weights_path = os.path.join(args.logs_path, args.model_id + '.h5') csv_path = os.path.join(args.logs_path, args.model_id + '.csv') download_extract_and_process_dataset(args.data_path) with tf.distribute.MirroredStrategy().scope(): recognizer = keras_ocr.recognition.Recognizer(alphabet=string.digits + string.ascii_lowercase, height=31, width=200, stn=False, optimizer=tf.keras.optimizers.RMSprop(), weights=None) if os.path.isfile(weights_path): print('Loading saved weights and creating new version.') dt_string = datetime.datetime.now().isoformat() weights_path = os.path.join(args.logs_path, args.model_id + '_' + dt_string + '.h5') csv_path = os.path.join(args.logs_path, args.model_id + '_' + dt_string + '.csv') recognizer.model.load_weights(weights_path) augmenter = imgaug.augmenters.Sequential([ imgaug.augmenters.Multiply((0.9, 1.1)), imgaug.augmenters.GammaContrast(gamma=(0.5, 3.0)), imgaug.augmenters.Invert(0.25, per_channel=0.5) ]) os.makedirs(args.logs_path, exist_ok=True) training_filepaths, validation_filepaths = [ get_filepaths(data_path=args.data_path, split=split) for split in ['train', 'val'] ] if args.verify_files: assert all( os.path.isfile(filepath) for filepath in tqdm.tqdm(training_filepaths + validation_filepaths, desc='Checking filepaths.')), 'Some files appear to be missing.' (training_image_generator, training_steps), (validation_image_generator, validation_steps) = [ (get_image_generator( filepaths=filepaths, augmenter=augmenter, width=recognizer.model.input_shape[2], height=recognizer.model.input_shape[1], ), math.ceil(len(filepaths) / args.batch_size)) for filepaths, augmenter in [(training_filepaths, augmenter), (validation_filepaths, None)] ] training_generator, validation_generator = [ tf.data.Dataset.from_generator( functools.partial(recognizer.get_batch_generator, image_generator=image_generator, batch_size=args.batch_size), output_types=((tf.float32, tf.int64, tf.float64, tf.int64), tf.float64), output_shapes=((tf.TensorShape([None, 31, 200, 1]), tf.TensorShape([None, recognizer.training_model.input_shape[1][1]]), tf.TensorShape([None, 1]), tf.TensorShape([None, 1])), tf.TensorShape([None, 1]))) for image_generator in [training_image_generator, validation_image_generator] ] callbacks = [ tf.keras.callbacks.EarlyStopping(monitor='val_loss', min_delta=0, patience=10, restore_best_weights=False), tf.keras.callbacks.ModelCheckpoint(weights_path, monitor='val_loss', save_best_only=True), tf.keras.callbacks.CSVLogger(csv_path) ] recognizer.training_model.fit( x=training_generator, steps_per_epoch=training_steps, validation_steps=validation_steps, validation_data=validation_generator, callbacks=callbacks, epochs=1000, )"""This script is what was used to generate thebackgrounds.zip and fonts.zip files."""# pylint: disable=invalid-name,redefined-outer-nameimport jsonimport urllib.requestimport urllib.parseimport concurrentimport shutilimport zipfileimport globimport osimport numpy as npimport tqdmimport cv2import keras_ocrif __name__ == '__main__': fonts_commit = 'a0726002eab4639ee96056a38cd35f6188011a81' fonts_sha256 = 'e447d23d24a5bbe8488200a058cd5b75b2acde525421c2e74dbfb90ceafce7bf' fonts_source_zip_filepath = keras_ocr.tools.download_and_verify( url=f'https://github.com/google/fonts/archive/{fonts_commit}.zip', cache_dir='.', sha256=fonts_sha256) shutil.rmtree('fonts-raw', ignore_errors=True) with zipfile.ZipFile(fonts_source_zip_filepath) as zfile: zfile.extractall(path='fonts-raw') retained_fonts = [] sha256s = [] basenames = [] # The blacklist includes fonts that, at least for the English alphabet, were found # to be illegible (e.g., thin fonts) or render in unexpected ways (e.g., mathematics # fonts). blacklist = [ 'AlmendraDisplay-Regular.ttf', 'RedactedScript-Bold.ttf', 'RedactedScript-Regular.ttf', 'Sevillana-Regular.ttf', 'Mplus1p-Thin.ttf', 'Stalemate-Regular.ttf', 'jsMath-cmsy10.ttf', 'Codystar-Regular.ttf', 'AdventPro-Thin.ttf', 'RoundedMplus1c-Thin.ttf', 'EncodeSans-Thin.ttf', 'AlegreyaSans-ThinItalic.ttf', 'AlegreyaSans-Thin.ttf', 'FiraSans-Thin.ttf', 'FiraSans-ThinItalic.ttf', 'WorkSans-Thin.ttf', 'Tomorrow-ThinItalic.ttf', 'Tomorrow-Thin.ttf', 'Italianno-Regular.ttf', 'IBMPlexSansCondensed-Thin.ttf', 'IBMPlexSansCondensed-ThinItalic.ttf', 'Lato-ExtraLightItalic.ttf', 'LibreBarcode128Text-Regular.ttf', 'LibreBarcode39-Regular.ttf', 'LibreBarcode39ExtendedText-Regular.ttf', 'EncodeSansExpanded-ExtraLight.ttf', 'Exo-Thin.ttf', 'Exo-ThinItalic.ttf', 'DrSugiyama-Regular.ttf', 'Taviraj-ThinItalic.ttf', 'SixCaps.ttf', 'IBMPlexSans-Thin.ttf', 'IBMPlexSans-ThinItalic.ttf', 'AdobeBlank-Regular.ttf', 'FiraSansExtraCondensed-ThinItalic.ttf', 'HeptaSlab[wght].ttf', 'Karla-Italic[wght].ttf', 'Karla[wght].ttf', 'RalewayDots-Regular.ttf', 'FiraSansCondensed-ThinItalic.ttf', 'jsMath-cmex10.ttf', 'LibreBarcode39Text-Regular.ttf', 'LibreBarcode39Extended-Regular.ttf', 'EricaOne-Regular.ttf', 'ArimaMadurai-Thin.ttf', 'IBMPlexSerif-ExtraLight.ttf', 'IBMPlexSerif-ExtraLightItalic.ttf', 'IBMPlexSerif-ThinItalic.ttf', 'IBMPlexSerif-Thin.ttf', 'Exo2-Thin.ttf', 'Exo2-ThinItalic.ttf', 'BungeeOutline-Regular.ttf', 'Redacted-Regular.ttf', 'JosefinSlab-ThinItalic.ttf', 'GothicA1-Thin.ttf', 'Kanit-ThinItalic.ttf', 'Kanit-Thin.ttf', 'AlegreyaSansSC-ThinItalic.ttf', 'AlegreyaSansSC-Thin.ttf', 'Chathura-Thin.ttf', 'Blinker-Thin.ttf', 'Italiana-Regular.ttf', 'Miama-Regular.ttf', 'Grenze-ThinItalic.ttf', 'LeagueScript-Regular.ttf', 'BigShouldersDisplay-Thin.ttf', 'YanoneKaffeesatz[wght].ttf', 'BungeeHairline-Regular.ttf', 'JosefinSans-Thin.ttf', 'JosefinSans-ThinItalic.ttf', 'Monofett.ttf', 'Raleway-ThinItalic.ttf', 'Raleway-Thin.ttf', 'JosefinSansStd-Light.ttf', 'LibreBarcode128-Regular.ttf' ] for filepath in tqdm.tqdm(sorted(glob.glob('fonts-raw/**/**/**/*.ttf')), desc='Filtering fonts.'): sha256 = keras_ocr.tools.sha256sum(filepath) basename = os.path.basename(filepath) # We check the sha256 and filenames because some of the fonts # in the repository are duplicated (see TRIVIA.md). if sha256 in sha256s or basename in basenames or basename in blacklist: continue sha256s.append(sha256) basenames.append(basename) retained_fonts.append(filepath) retained_font_families = set([filepath.split(os.sep)[-2] for filepath in retained_fonts]) added = [] with zipfile.ZipFile(file='fonts.zip', mode='w') as zfile: for font_family in tqdm.tqdm(retained_font_families, desc='Saving ZIP file.'): # We want to keep all the metadata files plus # the retained font files. And we don't want # to add the same file twice. files = [ input_filepath for input_filepath in glob.glob(f'fonts-raw/**/**/{font_family}/*') if input_filepath not in added and (input_filepath in retained_fonts or os.path.splitext(input_filepath)[1] != '.ttf') ] added.extend(files) for input_filepath in files: zfile.write(filename=input_filepath, arcname=os.path.join(*input_filepath.split(os.sep)[-2:])) print('Finished saving fonts file.') # pylint: disable=line-too-long url = ( 'https://commons.wikimedia.org/w/api.php?action=query&generator=categorymembers&gcmtype=file&format=json' '&gcmtitle=Category:Featured_pictures_on_Wikimedia_Commons&prop=imageinfo&gcmlimit=50&iiprop=url&iiurlwidth=1024' ) gcmcontinue = None max_responses = 300 responses = [] for responseCount in tqdm.tqdm(range(max_responses)): current_url = url if gcmcontinue is not None: current_url += f'&continue=gcmcontinue||&gcmcontinue={gcmcontinue}' with urllib.request.urlopen(url=current_url) as response: current = json.loads(response.read()) responses.append(current) gcmcontinue = None if 'continue' not in current else current['continue']['gcmcontinue'] if gcmcontinue is None: break print('Finished getting list of images.') # We want to avoid animated images as well as icon files. image_urls = [] for response in responses: image_urls.extend( [page['imageinfo'][0]['thumburl'] for page in response['query']['pages'].values()]) image_urls = [url for url in image_urls if url.lower().endswith('.jpg')] shutil.rmtree('backgrounds', ignore_errors=True) os.makedirs('backgrounds') assert len(image_urls) == len(set(image_urls)), 'Duplicates found!' with concurrent.futures.ThreadPoolExecutor(max_workers=2) as executor: futures = [ executor.submit(keras_ocr.tools.download_and_verify, url=url, cache_dir='./backgrounds', verbose=False) for url in image_urls ] for _ in tqdm.tqdm(concurrent.futures.as_completed(futures), total=len(futures)): pass for filepath in glob.glob('backgrounds/*.JPG'): os.rename(filepath, filepath.lower()) print('Filtering images by aspect ratio and maximum contiguous contour.') image_paths = np.array(sorted(glob.glob('backgrounds/*.jpg'))) def compute_metrics(filepath): image = keras_ocr.tools.read(filepath) aspect_ratio = image.shape[0] / image.shape[1] contour, _ = keras_ocr.tools.get_maximum_uniform_contour(image, fontsize=40) area = cv2.contourArea(contour) if contour is not None else 0 return aspect_ratio, area metrics = np.array([compute_metrics(filepath) for filepath in tqdm.tqdm(image_paths)]) filtered_paths = image_paths[(metrics[:, 0] < 3 / 2) & (metrics[:, 0] > 2 / 3) & (metrics[:, 1] > 1e6)] detector = keras_ocr.detection.Detector() paths_with_text = [ filepath for filepath in tqdm.tqdm(filtered_paths) if len( detector.detect( images=[keras_ocr.tools.read_and_fit(filepath, width=640, height=640)])[0]) > 0 ] filtered_paths = np.array([path for path in filtered_paths if path not in paths_with_text]) filtered_basenames = list(map(os.path.basename, filtered_paths)) basename_to_url = { os.path.basename(urllib.parse.urlparse(url).path).lower(): url for url in image_urls } filtered_urls = [basename_to_url[basename.lower()] for basename in filtered_basenames] assert len(filtered_urls) == len(filtered_paths) removed_paths = [filepath for filepath in image_paths if filepath not in filtered_paths] for filepath in removed_paths: os.remove(filepath) with open('backgrounds/urls.txt', 'w') as f: f.write('\n'.join(filtered_urls)) with zipfile.ZipFile(file='backgrounds.zip', mode='w') as zfile: for filepath in tqdm.tqdm(filtered_paths.tolist() + ['backgrounds/urls.txt'], desc='Saving ZIP file.'): zfile.write(filename=filepath, arcname=os.path.basename(filepath.lower()))

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