PaddleOCR是百度开源的超轻量级OCR模型库，提供了数十种文本检测、识别模型，旨在打造一套丰富、领先、实用的文字检测、识别模型/工具库，助力使用者训练出更好的模型，并应用落地。
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十分钟完成 PP-OCRv3 识别全流程实战
PaddleOCR是百度开源的超轻量级OCR模型库，提供了数十种文本检测、识别模型，旨在打造一套丰富、领先、实用的文字检测、识别模型/工具库，助力使用者训练出更好的模型，并应用落地。同时PaddleOCR也几经更新， 在2022.5.9 发布最新版本PaddleOCR release/2.5 ：
- 发布PP-OCRv3，速度可比情况下，中文场景效果相比于PP-OCRv2再提升5%，英文场景提升11%，80语种多语言模型平均识别准确率提升5%以上；
- 发布半自动标注工具PPOCRLabelv2：新增表格文字图像、图像关键信息抽取任务和不规则文字图像的标注功能；
- 发布OCR产业落地工具集：打通22种训练部署软硬件环境与方式，覆盖企业90%的训练部署环境需求；
- 发布交互式OCR开源电子书《动手学OCR》，覆盖OCR全栈技术的前沿理论与代码实践，并配套教学视频。
本教程旨在帮助使用者快速了解PP-OCRv3识别，并掌握其使用方式，包括：
- PP-OCR3识别快速使用
- 十分钟完成文本识别模型的训练和预测方式
最后带来PP-OCRv3的发版直播回放，快来收看吧！
1 PP-OCRv3识别快速使用
本节介绍如何使用PaddleOCR的轻量级模型完成文本识别的任务。
1.1 准备运行环境
首先，安装PaddleOCR的依赖库。
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import os # 修改代码运行的默认目录为 /home/aistudio/ os.chdir("/home/aistudio") # 如果git clone方式下载速度慢，您可直接在github中下载PaddleOCR的dygraph分支的zip压缩文件，然后上传到工作环境中解压使用 #!unzip PaddleOCR-dygraph.zip !git clone -b dygraph https://github.com/PaddlePaddle/PaddleOCR.gitIn [ ]
# 安装依赖库 os.chdir("/home/aistudio/PaddleOCR") !pip install -r requirements.txt -i https://mirror.baidu.com/pypi/simple1.2. 快速预测文字内容
测试图片：
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import os os.chdir('/home/aistudio/PaddleOCR') # 也可安装paddleocr whl包进行快速使用 # !pip install paddleocr from paddleocr import PaddleOCR ocr = PaddleOCR() # need to run only once to download and load model into memory img_path = '/home/aistudio/PaddleOCR/doc/imgs_words/en/word_1.png' result = ocr.ocr(img_path, det=False) for line in result: print(line)0%| | 0.00/3.67M [00:00download https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_infer.tar to /home/aistudio/.paddleocr/whl/det/ch/ch_PP-OCRv3_det_infer/ch_PP-OCRv3_det_infer.tar100%|██████████| 3.67M/3.67M [00:00<00:00, 6.36MiB/s] 0%| | 0.00/11.9M [00:00download https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_infer.tar to /home/aistudio/.paddleocr/whl/rec/ch/ch_PP-OCRv3_rec_infer/ch_PP-OCRv3_rec_infer.tar100%|██████████| 11.9M/11.9M [00:00<00:00, 42.0MiB/s] 19%|█▉ | 279k/1.45M [00:00<00:00, 2.67MiB/s]download https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar to /home/aistudio/.paddleocr/whl/cls/ch_ppocr_mobile_v2.0_cls_infer/ch_ppocr_mobile_v2.0_cls_infer.tar100%|██████████| 1.45M/1.45M [00:00<00:00, 4.66MiB/s][2022/05/05 14:53:56] ppocr DEBUG: Namespace(alpha=1.0, benchmark=False, beta=1.0, cls_batch_num=6, cls_image_shape='3, 48, 192', cls_model_dir='/home/aistudio/.paddleocr/whl/cls/ch_ppocr_mobile_v2.0_cls_infer', cls_thresh=0.9, cpu_threads=10, crop_res_save_dir='./output', det=True, det_algorithm='DB', det_db_box_thresh=0.6, det_db_score_mode='fast', det_db_thresh=0.3, det_db_unclip_ratio=1.5, det_east_cover_thresh=0.1, det_east_nms_thresh=0.2, det_east_score_thresh=0.8, det_fce_box_type='poly', det_limit_side_len=960, det_limit_type='max', det_model_dir='/home/aistudio/.paddleocr/whl/det/ch/ch_PP-OCRv3_det_infer', det_pse_box_thresh=0.85, det_pse_box_type='quad', det_pse_min_area=16, det_pse_scale=1, det_pse_thresh=0, det_sast_nms_thresh=0.2, det_sast_polygon=False, det_sast_score_thresh=0.5, draw_img_save_dir='./inference_results', drop_score=0.5, e2e_algorithm='PGNet', e2e_char_dict_path='./ppocr/utils/ic15_dict.txt', e2e_limit_side_len=768, e2e_limit_type='max', e2e_model_dir=None, e2e_pgnet_mode='fast', e2e_pgnet_score_thresh=0.5, e2e_pgnet_valid_set='totaltext', enable_mkldnn=False, fourier_degree=5, gpu_mem=500, help='==SUPPRESS==', image_dir=None, ir_optim=True, label_list=['0', '180'],, layout=True, layout_label_map=None, layout_path_model='lp://PubLayNet/ppyolov2_r50vd_dcn_365e_publaynet/config', max_batch_size=10, max_text_length=25, min_subgraph_size=15, mode='structure', ocr=True, ocr_version='PP-OCRv3', output='./output', precision='fp32', process_id=0, rec=True, rec_algorithm='CRNN', rec_batch_num=6, rec_char_dict_path='/home/aistudio/PaddleOCR/ppocr/utils/ppocr_keys_v1.txt', rec_image_shape='3, 32, 320', rec_model_dir='/home/aistudio/.paddleocr/whl/rec/ch/ch_PP-OCRv3_rec_infer', save_crop_res=False, save_log_path='./log_output/', scales=[8, 16, 32], show_log=True, structure_version='PP-STRUCTURE', table=True, table_char_dict_path=None, table_max_len=488, table_model_dir=None, total_process_num=1, type='ocr', use_angle_cls=False, use_dilation=False, use_gpu=True, use_mp=False, use_onnx=False, use_pdserving=False, use_space_char=True, use_tensorrt=False, vis_font_path='./doc/fonts/simfang.ttf', warmup=False)[2022/05/05 14:53:59] ppocr WARNING: Since the angle classifier is not initialized, the angle classifier will not be uesd during the forward process ('JOINT', 0.9179949760437012)2. 训练文字识别模型
本节提供了PaddleOCR文本识别任务的全流程指南，包括数据准备、模型训练、调优、评估、预测，各个阶段的详细说明：
2.1. 数据准备
2.1.1 自定义数据集
下面以通用数据集为例， 介绍如何准备数据集：
- 训练集
建议将训练图片放入同一个文件夹，并用一个txt文件（rec_gt_train.txt）记录图片路径和标签，txt文件里的内容如下:
注意： txt文件中默认请将图片路径和图片标签用 \t 分割，如用其他方式分割将造成训练报错。
" 图像文件名 图像标注信息 " train_data/rec/train/word_001.jpg 简单可依赖 train_data/rec/train/word_002.jpg 用科技让复杂的世界更简单 ...
最终训练集应有如下文件结构：
|-train_data |-rec |- rec_gt_train.txt |- train |- word_001.png |- word_002.jpg |- word_003.jpg | ...
除上述单张图像为一行格式之外，PaddleOCR也支持对离线增广后的数据进行训练，为了防止相同样本在同一个batch中被多次采样，我们可以将相同标签对应的图片路径写在一行中，以列表的形式给出，在训练中，PaddleOCR会随机选择列表中的一张图片进行训练。对应地，标注文件的格式如下。
["11.jpg", "12.jpg"] 简单可依赖 ["21.jpg", "22.jpg", "23.jpg"] 用科技让复杂的世界更简单 3.jpg ocr
上述示例标注文件中，"11.jpg"和"12.jpg"的标签相同，都是简单可依赖，在训练的时候，对于该行标注，会随机选择其中的一张图片进行训练。
- 验证集
同训练集类似，验证集也需要提供一个包含所有图片的文件夹（test）和一个rec_gt_test.txt，验证集的结构如下所示：
|-train_data |-rec |- rec_gt_test.txt |- test |- word_001.jpg |- word_002.jpg |- word_003.jpg | ...
2.1.2 数据下载
- ICDAR2015
若您本地没有数据集，可以在官网下载 ICDAR2015 数据，用于快速验证。也可以参考DTRB ，下载 benchmark 所需的lmdb格式数据集。
如果你使用的是icdar2015的公开数据集，PaddleOCR 提供了一份用于训练 ICDAR2015 数据集的标签文件，通过以下方式下载：
# 训练集标签 wget -P ./train_data/ic15_data https://paddleocr.bj.bcebos.com/dataset/rec_gt_train.txt # 测试集标签 wget -P ./train_data/ic15_data https://paddleocr.bj.bcebos.com/dataset/rec_gt_test.txt
PaddleOCR 也提供了数据格式转换脚本，可以将ICDAR官网 label 转换为PaddleOCR支持的数据格式。 数据转换工具在 ppocr/utils/gen_label.py, 这里以训练集为例：
# 将官网下载的标签文件转换为 rec_gt_label.txt python gen_label.py --mode="rec" --input_path="{path/of/origin/label}" --output_label="rec_gt_label.txt"数据样式格式如下，(a)为原始图片,(b)为每张图片对应的 Ground Truth 文本文件：
我们在 ~/data/data34824/ 目录下准备了数据集，可以使用如下指令解压数据文件。
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!mkdir train_data && cd ./train_data/ && mkdir -p ic15_data && cd ic15_data && cp ~/data/data34824/ic15_rec.zip ./ && unzip -o -q ic15_rec.zip && tar xf ic15.tar
2.2. 开始训练
PaddleOCR提供了训练脚本、评估脚本和预测脚本，本节将以 PP-OCRv3中文识别模型为例：
首先下载pretrain model，您可以下载训练好的模型在 icdar2015 数据上进行finetune
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!wget -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_train.tar # 解压模型参数 !cd ./pretrain_models/ && tar -xf ch_PP-OCRv3_rec_train.tar && rm -rf ch_PP-OCRv3_rec_train.tar
--2022-05-05 14:54:16-- https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_train.tar 正在解析主机 paddleocr.bj.bcebos.com (paddleocr.bj.bcebos.com)... 182.61.200.229, 182.61.200.195, 2409:8c04:1001:1002:0:ff:b001:368a 正在连接 paddleocr.bj.bcebos.com (paddleocr.bj.bcebos.com)|182.61.200.229|:443... 已连接。 已发出 HTTP 请求，正在等待回应... 200 OK 长度： 287467520 (274M) [application/x-tar] 正在保存至: “./pretrain_models/ch_PP-OCRv3_rec_train.tar” ch_PP-OCRv3_rec_tra 100%[===================>] 274.15M 47.9MB/s in 8.6s 2022-05-05 14:54:24 (31.9 MB/s) - 已保存 “./pretrain_models/ch_PP-OCRv3_rec_train.tar” [287467520/287467520])
2.2.1 启动训练
注意
需将configs/rec/PP-OCRv3/ch_PP-OCRv3_rec.yml中的训练和评估数据集路径修改为ic15的数据集路径：
Train: dataset: name: SimpleDataSet data_dir: ./train_data/ic15_data/ ext_op_transform_idx: 1 label_file_list: ["./train_data/ic15_data/rec_gt_train.txt"] ...... Eval: dataset: name: SimpleDataSet data_dir: ./train_data/ic15_data label_file_list: ["./train_data/ic15_data/rec_gt_test.txt"]
如果您安装的是cpu版本，请将配置文件中的 use_gpu 字段修改为false
启动训练命令很简单，指定好配置文件即可。另外在命令行中可以通过 -o 修改配置文件中的参数值。启动训练命令如下所示
其中：
- Global.pretrained_model: 加载的预训练模型路径
- Global.character_dict_path ： 字典路径（这里只支持26个小写字母+数字）
- Global.eval_batch_step ： 评估频率
- Global.epoch_num： 总训练轮数
如果训练速度慢，可去掉数据增强，但是当数据量较少，应用场景复杂时，建议保留数据增强，可提高模型泛化性和精度。
Train: dataset: name: SimpleDataSet data_dir: ./train_data/ic15_data/ ext_op_transform_idx: 1 label_file_list: ["./train_data/ic15_data/rec_gt_train.txt"] transforms: - DecodeImage: img_mode: BGR channel_first: false # - RecConAug: # prob: 0.5 # ext_data_num: 2 # image_shape: [48, 320, 3] # - RecAug:
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# 由于预训练模型提供的是蒸馏模型，需先将Student模型的参数提取出 import paddle params = paddle.load('./pretrain_models/ch_PP-OCRv3_rec_train/best_accuracy' + '.pdparams') new_state_dict = {} for k1 in params.keys(): if 'Student.' in k1: new_state_dict[k1.replace('Student.','')] = params[k1] # print(k1) paddle.save(new_state_dict, './pretrain_models/ch_PP-OCRv3_rec_train/best_accuracy'+'_new.pdparams') # CPU 训练 # 训练icdar15英文数据 训练日志会自动保存为 "{save_model_dir}" 下的train.log # !python3 tools/train.py -c configs/rec/PP-OCRv3/ch_PP-OCRv3_rec.yml \ # -o Global.pretrained_model=./pretrain_models/ch_PP-OCRv3_rec_train/best_accuracy_new Global.use_gpu=False \ # Global.character_dict_path=ppocr/utils/en_dict.txt \ # Global.eval_batch_step=[0,200] \ # Global.epoch_num=40 # GPU训练 支持单卡，多卡训练 #单卡训练 !python3 tools/train.py -c configs/rec/PP-OCRv3/ch_PP-OCRv3_rec.yml \ -o Global.pretrained_model=./pretrain_models/ch_PP-OCRv3_rec_train/best_accuracy_new\ Global.character_dict_path=ppocr/utils/en_dict.txt \ Global.eval_batch_step=[0,200] \ Global.epoch_num=40 #多卡训练，通过--gpus参数指定卡号 #!python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/rec/PP-OCRv3/ch_PP-OCRv3_rec.yml -o Global.pretrained_model=./pretrain_models/ch_PP-OCRv3_rec_train/best_accuracy2.3. 模型评估与预测
2.3.1 评估
训练中模型参数默认保存在Global.save_model_dir目录下。在评估指标时，需要设置Global.checkpoints指向保存的参数文件。评估数据集可以通过 configs/rec/PP-OCRv3/ch_PP-OCRv3_rec.yml 修改Eval中的 label_file_path 设置。
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# GPU 评估 !python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/PP-OCRv3/ch_PP-OCRv3_rec.yml -o Global.checkpoints=./output/rec_ppocr_v3/best_accuracy Global.character_dict_path=ppocr/utils/en_dict.txt # CPU 评估， Global.checkpoints 为待测权重 # !python3 tools/eval.py -c configs/rec/PP-OCRv3/ch_PP-OCRv3_rec.yml -o Global.checkpoints=./output/rec_ppocr_v3/best_accuracy Global.use_gpu=False Global.character_dict_path=ppocr/utils/en_dict.txt
----------- Configuration Arguments ----------- backend: auto elastic_server: None force: False gpus: 0 heter_devices: heter_worker_num: None heter_workers: host: None http_port: None ips: 127.0.0.1 job_id: None log_dir: log np: None nproc_per_node: None run_mode: None scale: 0 server_num: None servers: training_script: tools/eval.py training_script_args: ['-c', 'configs/rec/PP-OCRv3/ch_PP-OCRv3_rec.yml', '-o', 'Global.checkpoints=./output/rec_ppocr_v3/best_accuracy', 'Global.character_dict_path=ppocr/utils/en_dict.txt'] worker_num: None workers: ------------------------------------------------ WARNING 2022-05-05 16:08:53,089 launch.py:423] Not found distinct arguments and compiled with cuda or xpu. Default use collective mode launch train in GPU mode! INFO 2022-05-05 16:08:53,095 launch_utils.py:528] Local start 1 processes. First process distributed environment info (Only For Debug): +=======================================================================================+ | Distributed Envs Value | +---------------------------------------------------------------------------------------+ | PADDLE_TRAINER_ID 0 | | PADDLE_CURRENT_ENDPOINT 127.0.0.1:55453 | | PADDLE_TRAINERS_NUM 1 | | PADDLE_TRAINER_ENDPOINTS 127.0.0.1:55453 | | PADDLE_RANK_IN_NODE 0 | | PADDLE_LOCAL_DEVICE_IDS 0 | | PADDLE_WORLD_DEVICE_IDS 0 | | FLAGS_selected_gpus 0 | | FLAGS_selected_accelerators 0 | +=======================================================================================+ INFO 2022-05-05 16:08:53,095 launch_utils.py:532] details abouts PADDLE_TRAINER_ENDPOINTS can be found in log/endpoints.log, and detail running logs maybe found in log/workerlog.0 launch proc_id:10834 idx:0 [2022/05/05 16:08:54] ppocr INFO: Architecture : [2022/05/05 16:08:54] ppocr INFO: Backbone : [2022/05/05 16:08:54] ppocr INFO: last_conv_stride : [1, 2] [2022/05/05 16:08:54] ppocr INFO: last_pool_type : avg [2022/05/05 16:08:54] ppocr INFO: name : MobileNetV1Enhance [2022/05/05 16:08:54] ppocr INFO: scale : 0.5 [2022/05/05 16:08:54] ppocr INFO: Head : [2022/05/05 16:08:54] ppocr INFO: head_list : [2022/05/05 16:08:54] ppocr INFO: CTCHead : [2022/05/05 16:08:54] ppocr INFO: Head : [2022/05/05 16:08:54] ppocr INFO: fc_decay : 1e-05 [2022/05/05 16:08:54] ppocr INFO: Neck : [2022/05/05 16:08:54] ppocr INFO: depth : 2 [2022/05/05 16:08:54] ppocr INFO: dims : 64 [2022/05/05 16:08:54] ppocr INFO: hidden_dims : 120 [2022/05/05 16:08:54] ppocr INFO: name : svtr [2022/05/05 16:08:54] ppocr INFO: use_guide : True [2022/05/05 16:08:54] ppocr INFO: SARHead : [2022/05/05 16:08:54] ppocr INFO: enc_dim : 512 [2022/05/05 16:08:54] ppocr INFO: max_text_length : 25 [2022/05/05 16:08:54] ppocr INFO: name : MultiHead [2022/05/05 16:08:54] ppocr INFO: Transform : None [2022/05/05 16:08:54] ppocr INFO: algorithm : SVTR [2022/05/05 16:08:54] ppocr INFO: model_type : rec [2022/05/05 16:08:54] ppocr INFO: Eval : [2022/05/05 16:08:54] ppocr INFO: dataset : [2022/05/05 16:08:54] ppocr INFO: data_dir : ./train_data/ic15_data [2022/05/05 16:08:54] ppocr INFO: label_file_list : ['./train_data/ic15_data/rec_gt_test.txt'] [2022/05/05 16:08:54] ppocr INFO: name : SimpleDataSet [2022/05/05 16:08:54] ppocr INFO: transforms : [2022/05/05 16:08:54] ppocr INFO: DecodeImage : [2022/05/05 16:08:54] ppocr INFO: channel_first : False [2022/05/05 16:08:54] ppocr INFO: img_mode : BGR [2022/05/05 16:08:54] ppocr INFO: MultiLabelEncode : None [2022/05/05 16:08:54] ppocr INFO: RecResizeImg : [2022/05/05 16:08:54] ppocr INFO: image_shape : [3, 48, 320] [2022/05/05 16:08:54] ppocr INFO: KeepKeys : [2022/05/05 16:08:54] ppocr INFO: keep_keys : ['image', 'label_ctc', 'label_sar', 'length', 'valid_ratio'] [2022/05/05 16:08:54] ppocr INFO: loader : [2022/05/05 16:08:54] ppocr INFO: batch_size_per_card : 128 [2022/05/05 16:08:54] ppocr INFO: drop_last : False [2022/05/05 16:08:54] ppocr INFO: num_workers : 4 [2022/05/05 16:08:54] ppocr INFO: shuffle : False [2022/05/05 16:08:54] ppocr INFO: Global : [2022/05/05 16:08:54] ppocr INFO: cal_metric_during_train : True [2022/05/05 16:08:54] ppocr INFO: character_dict_path : ppocr/utils/en_dict.txt [2022/05/05 16:08:54] ppocr INFO: checkpoints : ./output/rec_ppocr_v3/best_accuracy [2022/05/05 16:08:54] ppocr INFO: debug : False [2022/05/05 16:08:54] ppocr INFO: distributed : False [2022/05/05 16:08:54] ppocr INFO: epoch_num : 500 [2022/05/05 16:08:54] ppocr INFO: eval_batch_step : [0, 2000] [2022/05/05 16:08:54] ppocr INFO: infer_img : doc/imgs_words/ch/word_1.jpg [2022/05/05 16:08:54] ppocr INFO: infer_mode : False [2022/05/05 16:08:54] ppocr INFO: log_smooth_window : 20 [2022/05/05 16:08:54] ppocr INFO: max_text_length : 25 [2022/05/05 16:08:54] ppocr INFO: pretrained_model : None [2022/05/05 16:08:54] ppocr INFO: print_batch_step : 10 [2022/05/05 16:08:54] ppocr INFO: save_epoch_step : 3 [2022/05/05 16:08:54] ppocr INFO: save_inference_dir : None [2022/05/05 16:08:54] ppocr INFO: save_model_dir : ./output/rec_ppocr_v3 [2022/05/05 16:08:54] ppocr INFO: save_res_path : ./output/rec/predicts_ppocrv3.txt [2022/05/05 16:08:54] ppocr INFO: use_gpu : True [2022/05/05 16:08:54] ppocr INFO: use_space_char : True [2022/05/05 16:08:54] ppocr INFO: use_visualdl : False [2022/05/05 16:08:54] ppocr INFO: Loss : [2022/05/05 16:08:54] ppocr INFO: loss_config_list : [2022/05/05 16:08:54] ppocr INFO: CTCLoss : None [2022/05/05 16:08:54] ppocr INFO: SARLoss : None [2022/05/05 16:08:54] ppocr INFO: name : MultiLoss [2022/05/05 16:08:54] ppocr INFO: Metric : [2022/05/05 16:08:54] ppocr INFO: ignore_space : False [2022/05/05 16:08:54] ppocr INFO: main_indicator : acc [2022/05/05 16:08:54] ppocr INFO: name : RecMetric [2022/05/05 16:08:54] ppocr INFO: Optimizer : [2022/05/05 16:08:54] ppocr INFO: beta1 : 0.9 [2022/05/05 16:08:54] ppocr INFO: beta2 : 0.999 [2022/05/05 16:08:54] ppocr INFO: lr : [2022/05/05 16:08:54] ppocr INFO: learning_rate : 0.001 [2022/05/05 16:08:54] ppocr INFO: name : Cosine [2022/05/05 16:08:54] ppocr INFO: warmup_epoch : 5 [2022/05/05 16:08:54] ppocr INFO: name : Adam [2022/05/05 16:08:54] ppocr INFO: regularizer : [2022/05/05 16:08:54] ppocr INFO: factor : 3e-05 [2022/05/05 16:08:54] ppocr INFO: name : L2 [2022/05/05 16:08:54] ppocr INFO: PostProcess : [2022/05/05 16:08:54] ppocr INFO: name : CTCLabelDecode [2022/05/05 16:08:54] ppocr INFO: Train : [2022/05/05 16:08:54] ppocr INFO: dataset : [2022/05/05 16:08:54] ppocr INFO: data_dir : ./train_data/ic15_data/ [2022/05/05 16:08:54] ppocr INFO: ext_op_transform_idx : 1 [2022/05/05 16:08:54] ppocr INFO: label_file_list : ['./train_data/ic15_data/rec_gt_train.txt'] [2022/05/05 16:08:54] ppocr INFO: name : SimpleDataSet [2022/05/05 16:08:54] ppocr INFO: transforms : [2022/05/05 16:08:54] ppocr INFO: DecodeImage : [2022/05/05 16:08:54] ppocr INFO: channel_first : False [2022/05/05 16:08:54] ppocr INFO: img_mode : BGR [2022/05/05 16:08:54] ppocr INFO: MultiLabelEncode : None [2022/05/05 16:08:54] ppocr INFO: RecResizeImg : [2022/05/05 16:08:54] ppocr INFO: image_shape : [3, 48, 320] [2022/05/05 16:08:54] ppocr INFO: KeepKeys : [2022/05/05 16:08:54] ppocr INFO: keep_keys : ['image', 'label_ctc', 'label_sar', 'length', 'valid_ratio'] [2022/05/05 16:08:54] ppocr INFO: loader : [2022/05/05 16:08:54] ppocr INFO: batch_size_per_card : 128 [2022/05/05 16:08:54] ppocr INFO: drop_last : True [2022/05/05 16:08:54] ppocr INFO: num_workers : 4 [2022/05/05 16:08:54] ppocr INFO: shuffle : True [2022/05/05 16:08:54] ppocr INFO: profiler_options : None [2022/05/05 16:08:54] ppocr INFO: train with paddle 2.2.2 and device CUDAPlace(0) [2022/05/05 16:08:54] ppocr INFO: Initialize indexs of datasets:['./train_data/ic15_data/rec_gt_test.txt'] W0505 16:08:54.870386 10834 device_context.cc:447] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 10.1, Runtime API Version: 10.1 W0505 16:08:54.875574 10834 device_context.cc:465] device: 0, cuDNN Version: 7.6. [2022/05/05 16:08:59] ppocr INFO: resume from ./output/rec_ppocr_v3/best_accuracy [2022/05/05 16:08:59] ppocr INFO: metric in ckpt *************** [2022/05/05 16:08:59] ppocr INFO: acc:0.5551275851943784 [2022/05/05 16:08:59] ppocr INFO: norm_edit_dis:0.8100207578002598 [2022/05/05 16:08:59] ppocr INFO: fps:1855.1658462248745 [2022/05/05 16:08:59] ppocr INFO: best_epoch:36 [2022/05/05 16:08:59] ppocr INFO: start_epoch:37 eval model:: 0%| | 0/17 [00:002.3.2 测试识别效果
使用 PaddleOCR 训练好的模型，可以通过以下脚本进行快速预测。
默认预测图片存储在 infer_img 里，通过 -o Global.checkpoints 加载训练好的参数文件：
根据配置文件中设置的的 save_model_dir 和 save_epoch_step 字段，会有以下几种参数被保存下来：
output/rec/ ├── best_accuracy.pdopt ├── best_accuracy.pdparams ├── best_accuracy.states ├── config.yml ├── iter_epoch_3.pdopt ├── iter_epoch_3.pdparams ├── iter_epoch_3.states ├── latest.pdopt ├── latest.pdparams ├── latest.states └── train.log其中 best_accuracy.* 是评估集上的最优模型；iter_epoch_x.* 是以 save_epoch_step 为间隔保存下来的模型；latest.* 是最后一个epoch的模型。
In [23]
# 预测英文结果 # GPU预测 !python3 tools/infer_rec.py -c configs/rec/PP-OCRv3/ch_PP-OCRv3_rec.yml -o Global.pretrained_model=./output/rec_ppocr_v3/best_accuracy Global.character_dict_path=ppocr/utils/en_dict.txt Global.infer_img=doc/imgs_words/en/word_1.png[2022/05/05 16:09:44] ppocr INFO: Architecture : [2022/05/05 16:09:44] ppocr INFO: Backbone : [2022/05/05 16:09:44] ppocr INFO: last_conv_stride : [1, 2] [2022/05/05 16:09:44] ppocr INFO: last_pool_type : avg [2022/05/05 16:09:44] ppocr INFO: name : MobileNetV1Enhance [2022/05/05 16:09:44] ppocr INFO: scale : 0.5 [2022/05/05 16:09:44] ppocr INFO: Head : [2022/05/05 16:09:44] ppocr INFO: head_list : [2022/05/05 16:09:44] ppocr INFO: CTCHead : [2022/05/05 16:09:44] ppocr INFO: Head : [2022/05/05 16:09:44] ppocr INFO: fc_decay : 1e-05 [2022/05/05 16:09:44] ppocr INFO: Neck : [2022/05/05 16:09:44] ppocr INFO: depth : 2 [2022/05/05 16:09:44] ppocr INFO: dims : 64 [2022/05/05 16:09:44] ppocr INFO: hidden_dims : 120 [2022/05/05 16:09:44] ppocr INFO: name : svtr [2022/05/05 16:09:44] ppocr INFO: use_guide : True [2022/05/05 16:09:44] ppocr INFO: SARHead : [2022/05/05 16:09:44] ppocr INFO: enc_dim : 512 [2022/05/05 16:09:44] ppocr INFO: max_text_length : 25 [2022/05/05 16:09:44] ppocr INFO: name : MultiHead [2022/05/05 16:09:44] ppocr INFO: Transform : None [2022/05/05 16:09:44] ppocr INFO: algorithm : SVTR [2022/05/05 16:09:44] ppocr INFO: model_type : rec [2022/05/05 16:09:44] ppocr INFO: Eval : [2022/05/05 16:09:44] ppocr INFO: dataset : [2022/05/05 16:09:44] ppocr INFO: data_dir : ./train_data/ic15_data [2022/05/05 16:09:44] ppocr INFO: label_file_list : ['./train_data/ic15_data/rec_gt_test.txt'] [2022/05/05 16:09:44] ppocr INFO: name : SimpleDataSet [2022/05/05 16:09:44] ppocr INFO: transforms : [2022/05/05 16:09:44] ppocr INFO: DecodeImage : [2022/05/05 16:09:44] ppocr INFO: channel_first : False [2022/05/05 16:09:44] ppocr INFO: img_mode : BGR [2022/05/05 16:09:44] ppocr INFO: MultiLabelEncode : None [2022/05/05 16:09:44] ppocr INFO: RecResizeImg : [2022/05/05 16:09:44] ppocr INFO: image_shape : [3, 48, 320] [2022/05/05 16:09:44] ppocr INFO: KeepKeys : [2022/05/05 16:09:44] ppocr INFO: keep_keys : ['image', 'label_ctc', 'label_sar', 'length', 'valid_ratio'] [2022/05/05 16:09:44] ppocr INFO: loader : [2022/05/05 16:09:44] ppocr INFO: batch_size_per_card : 128 [2022/05/05 16:09:44] ppocr INFO: drop_last : False [2022/05/05 16:09:44] ppocr INFO: num_workers : 4 [2022/05/05 16:09:44] ppocr INFO: shuffle : False [2022/05/05 16:09:44] ppocr INFO: Global : [2022/05/05 16:09:44] ppocr INFO: cal_metric_during_train : True [2022/05/05 16:09:44] ppocr INFO: character_dict_path : ppocr/utils/en_dict.txt [2022/05/05 16:09:44] ppocr INFO: checkpoints : None [2022/05/05 16:09:44] ppocr INFO: debug : False [2022/05/05 16:09:44] ppocr INFO: distributed : False [2022/05/05 16:09:44] ppocr INFO: epoch_num : 500 [2022/05/05 16:09:44] ppocr INFO: eval_batch_step : [0, 2000] [2022/05/05 16:09:44] ppocr INFO: infer_img : doc/imgs_words/en/word_1.png [2022/05/05 16:09:44] ppocr INFO: infer_mode : False [2022/05/05 16:09:44] ppocr INFO: log_smooth_window : 20 [2022/05/05 16:09:44] ppocr INFO: max_text_length : 25 [2022/05/05 16:09:44] ppocr INFO: pretrained_model : ./output/rec_ppocr_v3/best_accuracy [2022/05/05 16:09:44] ppocr INFO: print_batch_step : 10 [2022/05/05 16:09:44] ppocr INFO: save_epoch_step : 3 [2022/05/05 16:09:44] ppocr INFO: save_inference_dir : None [2022/05/05 16:09:44] ppocr INFO: save_model_dir : ./output/rec_ppocr_v3 [2022/05/05 16:09:44] ppocr INFO: save_res_path : ./output/rec/predicts_ppocrv3.txt [2022/05/05 16:09:44] ppocr INFO: use_gpu : True [2022/05/05 16:09:44] ppocr INFO: use_space_char : True [2022/05/05 16:09:44] ppocr INFO: use_visualdl : False [2022/05/05 16:09:44] ppocr INFO: Loss : [2022/05/05 16:09:44] ppocr INFO: loss_config_list : [2022/05/05 16:09:44] ppocr INFO: CTCLoss : None [2022/05/05 16:09:44] ppocr INFO: SARLoss : None [2022/05/05 16:09:44] ppocr INFO: name : MultiLoss [2022/05/05 16:09:44] ppocr INFO: Metric : [2022/05/05 16:09:44] ppocr INFO: ignore_space : False [2022/05/05 16:09:44] ppocr INFO: main_indicator : acc [2022/05/05 16:09:44] ppocr INFO: name : RecMetric [2022/05/05 16:09:44] ppocr INFO: Optimizer : [2022/05/05 16:09:44] ppocr INFO: beta1 : 0.9 [2022/05/05 16:09:44] ppocr INFO: beta2 : 0.999 [2022/05/05 16:09:44] ppocr INFO: lr : [2022/05/05 16:09:44] ppocr INFO: learning_rate : 0.001 [2022/05/05 16:09:44] ppocr INFO: name : Cosine [2022/05/05 16:09:44] ppocr INFO: warmup_epoch : 5 [2022/05/05 16:09:44] ppocr INFO: name : Adam [2022/05/05 16:09:44] ppocr INFO: regularizer : [2022/05/05 16:09:44] ppocr INFO: factor : 3e-05 [2022/05/05 16:09:44] ppocr INFO: name : L2 [2022/05/05 16:09:44] ppocr INFO: PostProcess : [2022/05/05 16:09:44] ppocr INFO: name : CTCLabelDecode [2022/05/05 16:09:44] ppocr INFO: Train : [2022/05/05 16:09:44] ppocr INFO: dataset : [2022/05/05 16:09:44] ppocr INFO: data_dir : ./train_data/ic15_data/ [2022/05/05 16:09:44] ppocr INFO: ext_op_transform_idx : 1 [2022/05/05 16:09:44] ppocr INFO: label_file_list : ['./train_data/ic15_data/rec_gt_train.txt'] [2022/05/05 16:09:44] ppocr INFO: name : SimpleDataSet [2022/05/05 16:09:44] ppocr INFO: transforms : [2022/05/05 16:09:44] ppocr INFO: DecodeImage : [2022/05/05 16:09:44] ppocr INFO: channel_first : False [2022/05/05 16:09:44] ppocr INFO: img_mode : BGR [2022/05/05 16:09:44] ppocr INFO: MultiLabelEncode : None [2022/05/05 16:09:44] ppocr INFO: RecResizeImg : [2022/05/05 16:09:44] ppocr INFO: image_shape : [3, 48, 320] [2022/05/05 16:09:44] ppocr INFO: KeepKeys : [2022/05/05 16:09:44] ppocr INFO: keep_keys : ['image', 'label_ctc', 'label_sar', 'length', 'valid_ratio'] [2022/05/05 16:09:44] ppocr INFO: loader : [2022/05/05 16:09:44] ppocr INFO: batch_size_per_card : 128 [2022/05/05 16:09:44] ppocr INFO: drop_last : True [2022/05/05 16:09:44] ppocr INFO: num_workers : 4 [2022/05/05 16:09:44] ppocr INFO: shuffle : True [2022/05/05 16:09:44] ppocr INFO: profiler_options : None [2022/05/05 16:09:44] ppocr INFO: train with paddle 2.2.2 and device CUDAPlace(0) W0505 16:09:44.179414 10923 device_context.cc:447] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 10.1, Runtime API Version: 10.1 W0505 16:09:44.184576 10923 device_context.cc:465] device: 0, cuDNN Version: 7.6. [2022/05/05 16:09:48] ppocr INFO: load pretrain successful from ./output/rec_ppocr_v3/best_accuracy [2022/05/05 16:09:48] ppocr INFO: infer_img: doc/imgs_words/en/word_1.png [2022/05/05 16:09:48] ppocr INFO: result: JOINT 0.9950313568115234 [2022/05/05 16:09:48] ppocr INFO: success!预测图片：
预测使用的配置文件必须与训练一致.
测试文件夹下所有图像的文字识别效果
In [24]
# GPU预测 !python3 tools/infer_rec.py -c configs/rec/PP-OCRv3/ch_PP-OCRv3_rec.yml -o Global.pretrained_model=./output/rec_ppocr_v3/best_accuracy Global.character_dict_path=ppocr/utils/en_dict.txt Global.infer_img=./doc/imgs_words_en/[2022/05/05 16:09:59] ppocr INFO: Architecture : [2022/05/05 16:09:59] ppocr INFO: Backbone : [2022/05/05 16:09:59] ppocr INFO: last_conv_stride : [1, 2] [2022/05/05 16:09:59] ppocr INFO: last_pool_type : avg [2022/05/05 16:09:59] ppocr INFO: name : MobileNetV1Enhance [2022/05/05 16:09:59] ppocr INFO: scale : 0.5 [2022/05/05 16:09:59] ppocr INFO: Head : [2022/05/05 16:09:59] ppocr INFO: head_list : [2022/05/05 16:09:59] ppocr INFO: CTCHead : [2022/05/05 16:09:59] ppocr INFO: Head : [2022/05/05 16:09:59] ppocr INFO: fc_decay : 1e-05 [2022/05/05 16:09:59] ppocr INFO: Neck : [2022/05/05 16:09:59] ppocr INFO: depth : 2 [2022/05/05 16:09:59] ppocr INFO: dims : 64 [2022/05/05 16:09:59] ppocr INFO: hidden_dims : 120 [2022/05/05 16:09:59] ppocr INFO: name : svtr [2022/05/05 16:09:59] ppocr INFO: use_guide : True [2022/05/05 16:09:59] ppocr INFO: SARHead : [2022/05/05 16:09:59] ppocr INFO: enc_dim : 512 [2022/05/05 16:09:59] ppocr INFO: max_text_length : 25 [2022/05/05 16:09:59] ppocr INFO: name : MultiHead [2022/05/05 16:09:59] ppocr INFO: Transform : None [2022/05/05 16:09:59] ppocr INFO: algorithm : SVTR [2022/05/05 16:09:59] ppocr INFO: model_type : rec [2022/05/05 16:09:59] ppocr INFO: Eval : [2022/05/05 16:09:59] ppocr INFO: dataset : [2022/05/05 16:09:59] ppocr INFO: data_dir : ./train_data/ic15_data [2022/05/05 16:09:59] ppocr INFO: label_file_list : ['./train_data/ic15_data/rec_gt_test.txt'] [2022/05/05 16:09:59] ppocr INFO: name : SimpleDataSet [2022/05/05 16:09:59] ppocr INFO: transforms : [2022/05/05 16:09:59] ppocr INFO: DecodeImage : [2022/05/05 16:09:59] ppocr INFO: channel_first : False [2022/05/05 16:09:59] ppocr INFO: img_mode : BGR [2022/05/05 16:09:59] ppocr INFO: MultiLabelEncode : None [2022/05/05 16:09:59] ppocr INFO: RecResizeImg : [2022/05/05 16:09:59] ppocr INFO: image_shape : [3, 48, 320] [2022/05/05 16:09:59] ppocr INFO: KeepKeys : [2022/05/05 16:09:59] ppocr INFO: keep_keys : ['image', 'label_ctc', 'label_sar', 'length', 'valid_ratio'] [2022/05/05 16:09:59] ppocr INFO: loader : [2022/05/05 16:09:59] ppocr INFO: batch_size_per_card : 128 [2022/05/05 16:09:59] ppocr INFO: drop_last : False [2022/05/05 16:09:59] ppocr INFO: num_workers : 4 [2022/05/05 16:09:59] ppocr INFO: shuffle : False [2022/05/05 16:09:59] ppocr INFO: Global : [2022/05/05 16:09:59] ppocr INFO: cal_metric_during_train : True [2022/05/05 16:09:59] ppocr INFO: character_dict_path : ppocr/utils/en_dict.txt [2022/05/05 16:09:59] ppocr INFO: checkpoints : None [2022/05/05 16:09:59] ppocr INFO: debug : False [2022/05/05 16:09:59] ppocr INFO: distributed : False [2022/05/05 16:09:59] ppocr INFO: epoch_num : 500 [2022/05/05 16:09:59] ppocr INFO: eval_batch_step : [0, 2000] [2022/05/05 16:09:59] ppocr INFO: infer_img : ./doc/imgs_words_en/ [2022/05/05 16:09:59] ppocr INFO: infer_mode : False [2022/05/05 16:09:59] ppocr INFO: log_smooth_window : 20 [2022/05/05 16:09:59] ppocr INFO: max_text_length : 25 [2022/05/05 16:09:59] ppocr INFO: pretrained_model : ./output/rec_ppocr_v3/best_accuracy [2022/05/05 16:09:59] ppocr INFO: print_batch_step : 10 [2022/05/05 16:09:59] ppocr INFO: save_epoch_step : 3 [2022/05/05 16:09:59] ppocr INFO: save_inference_dir : None [2022/05/05 16:09:59] ppocr INFO: save_model_dir : ./output/rec_ppocr_v3 [2022/05/05 16:09:59] ppocr INFO: save_res_path : ./output/rec/predicts_ppocrv3.txt [2022/05/05 16:09:59] ppocr INFO: use_gpu : True [2022/05/05 16:09:59] ppocr INFO: use_space_char : True [2022/05/05 16:09:59] ppocr INFO: use_visualdl : False [2022/05/05 16:09:59] ppocr INFO: Loss : [2022/05/05 16:09:59] ppocr INFO: loss_config_list : [2022/05/05 16:09:59] ppocr INFO: CTCLoss : None [2022/05/05 16:09:59] ppocr INFO: SARLoss : None [2022/05/05 16:09:59] ppocr INFO: name : MultiLoss [2022/05/05 16:09:59] ppocr INFO: Metric : [2022/05/05 16:09:59] ppocr INFO: ignore_space : False [2022/05/05 16:09:59] ppocr INFO: main_indicator : acc [2022/05/05 16:09:59] ppocr INFO: name : RecMetric [2022/05/05 16:09:59] ppocr INFO: Optimizer : [2022/05/05 16:09:59] ppocr INFO: beta1 : 0.9 [2022/05/05 16:09:59] ppocr INFO: beta2 : 0.999 [2022/05/05 16:09:59] ppocr INFO: lr : [2022/05/05 16:09:59] ppocr INFO: learning_rate : 0.001 [2022/05/05 16:09:59] ppocr INFO: name : Cosine [2022/05/05 16:09:59] ppocr INFO: warmup_epoch : 5 [2022/05/05 16:09:59] ppocr INFO: name : Adam [2022/05/05 16:09:59] ppocr INFO: regularizer : [2022/05/05 16:09:59] ppocr INFO: factor : 3e-05 [2022/05/05 16:09:59] ppocr INFO: name : L2 [2022/05/05 16:09:59] ppocr INFO: PostProcess : [2022/05/05 16:09:59] ppocr INFO: name : CTCLabelDecode [2022/05/05 16:09:59] ppocr INFO: Train : [2022/05/05 16:09:59] ppocr INFO: dataset : [2022/05/05 16:09:59] ppocr INFO: data_dir : ./train_data/ic15_data/ [2022/05/05 16:09:59] ppocr INFO: ext_op_transform_idx : 1 [2022/05/05 16:09:59] ppocr INFO: label_file_list : ['./train_data/ic15_data/rec_gt_train.txt'] [2022/05/05 16:09:59] ppocr INFO: name : SimpleDataSet [2022/05/05 16:09:59] ppocr INFO: transforms : [2022/05/05 16:09:59] ppocr INFO: DecodeImage : [2022/05/05 16:09:59] ppocr INFO: channel_first : False [2022/05/05 16:09:59] ppocr INFO: img_mode : BGR [2022/05/05 16:09:59] ppocr INFO: MultiLabelEncode : None [2022/05/05 16:09:59] ppocr INFO: RecResizeImg : [2022/05/05 16:09:59] ppocr INFO: image_shape : [3, 48, 320] [2022/05/05 16:09:59] ppocr INFO: KeepKeys : [2022/05/05 16:09:59] ppocr INFO: keep_keys : ['image', 'label_ctc', 'label_sar', 'length', 'valid_ratio'] [2022/05/05 16:09:59] ppocr INFO: loader : [2022/05/05 16:09:59] ppocr INFO: batch_size_per_card : 128 [2022/05/05 16:09:59] ppocr INFO: drop_last : True [2022/05/05 16:09:59] ppocr INFO: num_workers : 4 [2022/05/05 16:09:59] ppocr INFO: shuffle : True [2022/05/05 16:09:59] ppocr INFO: profiler_options : None [2022/05/05 16:09:59] ppocr INFO: train with paddle 2.2.2 and device CUDAPlace(0) W0505 16:09:59.636674 10950 device_context.cc:447] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 10.1, Runtime API Version: 10.1 W0505 16:09:59.641562 10950 device_context.cc:465] device: 0, cuDNN Version: 7.6. [2022/05/05 16:10:04] ppocr INFO: load pretrain successful from ./output/rec_ppocr_v3/best_accuracy [2022/05/05 16:10:04] ppocr INFO: infer_img: ./doc/imgs_words_en/word_10.png [2022/05/05 16:10:04] ppocr INFO: result: PAIN 0.9976047277450562 [2022/05/05 16:10:04] ppocr INFO: infer_img: ./doc/imgs_words_en/word_116.png [2022/05/05 16:10:04] ppocr INFO: result: QBHOUSE 0.9709253311157227 [2022/05/05 16:10:04] ppocr INFO: infer_img: ./doc/imgs_words_en/word_19.png [2022/05/05 16:10:04] ppocr INFO: result: SLOW 0.9971550703048706 [2022/05/05 16:10:04] ppocr INFO: infer_img: ./doc/imgs_words_en/word_201.png [2022/05/05 16:10:04] ppocr INFO: result: HOUSE 0.9960419535636902 [2022/05/05 16:10:04] ppocr INFO: infer_img: ./doc/imgs_words_en/word_308.png [2022/05/05 16:10:04] ppocr INFO: result: LITTLE 0.9545474052429199 [2022/05/05 16:10:04] ppocr INFO: infer_img: ./doc/imgs_words_en/word_336.png [2022/05/05 16:10:04] ppocr INFO: result: SUPER 0.9802681803703308 [2022/05/05 16:10:04] ppocr INFO: infer_img: ./doc/imgs_words_en/word_401.png [2022/05/05 16:10:04] ppocr INFO: result: BURGE 0.827716052532196 [2022/05/05 16:10:04] ppocr INFO: infer_img: ./doc/imgs_words_en/word_461.png [2022/05/05 16:10:04] ppocr INFO: result: SPED 0.912112832069397 [2022/05/05 16:10:04] ppocr INFO: infer_img: ./doc/imgs_words_en/word_52.png [2022/05/05 16:10:04] ppocr INFO: result: Future 0.9685637354850769 [2022/05/05 16:10:04] ppocr INFO: infer_img: ./doc/imgs_words_en/word_545.png [2022/05/05 16:10:04] ppocr INFO: result: EORIT 0.9076364636421204 [2022/05/05 16:10:04] ppocr INFO: success!2.4. 模型导出与预测
inference 模型（paddle.jit.save保存的模型） 一般是模型训练，把模型结构和模型参数保存在文件中的固化模型，多用于预测部署场景。 训练过程中保存的模型是checkpoints模型，保存的只有模型的参数，多用于恢复训练等。 与checkpoints模型相比，inference 模型会额外保存模型的结构信息，在预测部署、加速推理上性能优越，灵活方便，适合于实际系统集成。
识别模型转inference模型与检测的方式相同，如下：
In [25]
# -c 后面设置训练算法的yml配置文件 # -o 配置可选参数 # Global.pretrained_model 参数设置待转换的训练模型地址，不用添加文件后缀 .pdmodel，.pdopt或.pdparams。 # Global.save_inference_dir参数设置转换的模型将保存的地址。 !python3 tools/export_model.py -c configs/rec/PP-OCRv3/ch_PP-OCRv3_rec.yml -o Global.pretrained_model=./output/rec_ppocr_v3/best_accuracy Global.character_dict_path=ppocr/utils/en_dict.txt Global.save_inference_dir=./inference/ch_PP-OCRv3_rec/W0505 16:10:41.224627 11024 device_context.cc:447] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 10.1, Runtime API Version: 10.1 W0505 16:10:41.229585 11024 device_context.cc:465] device: 0, cuDNN Version: 7.6. [2022/05/05 16:10:45] ppocr INFO: load pretrain successful from ./output/rec_ppocr_v3/best_accuracy [2022/05/05 16:10:47] ppocr INFO: inference model is saved to ./inference/ch_PP-OCRv3_rec/inference**注意：**如果您是在自己的数据集上训练的模型，并且调整了中文字符的字典文件，请注意修改配置文件中的character_dict_path为自定义字典文件。
转换成功后，在目录下有三个文件：
inference/ch_PP-OCRv3_rec/ ├── inference.pdiparams # 识别inference模型的参数文件 ├── inference.pdiparams.info # 识别inference模型的参数信息，可忽略 └── inference.pdmodel # 识别inference模型的program文件
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自定义模型推理
如果训练时修改了文本的字典，在使用inference模型预测时，需要通过--rec_char_dict_path指定使用的字典路径
注意
- 使用PP-OCRv3识别进行推理时，不需要使用--rec_algorithm指定算法名称，使用默认的推理方式即为PP-OCRv3识别的推理过程。
In [27]
# GPU预测 !python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" --rec_model_dir=./inference/ch_PP-OCRv3_rec/ --rec_char_dict_path=ppocr/utils/en_dict.txt # CPU预测 !python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" --rec_model_dir=./inference/ch_PP-OCRv3_rec/ --rec_char_dict_path=ppocr/utils/en_dict.txt --use_gpu=False
[2022/05/05 16:13:50] ppocr INFO: Predicts of ./doc/imgs_words_en/word_336.png:('SUPER', 0.9802668690681458) [2022/05/05 16:13:53] ppocr INFO: Predicts of ./doc/imgs_words_en/word_336.png:('SUPER', 0.9802700281143188)推理预测的图片为：
3. FAQ
Q1: 训练模型转inference 模型之后预测效果不一致？
A：此类问题出现较多，问题多是trained model预测时候的预处理、后处理参数和inference model预测的时候的预处理、后处理参数不一致导致
Q2: 如何自定义字典、修改backbone、训练多语言模型？
A：请参考PP-OCRv3识别详细教程。
