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| # Model Architecture Summary | |
| MMOCR has implemented many models that support various tasks. Depending on the type of tasks, these models have different architectural designs and, therefore, might be a bit confusing for beginners to master. We release a primary design doc to clearly illustrate the basic task-specific architectures and provide quick pointers to docstrings of model components to aid users' understanding. | |
| ## Text Detection Models | |
| <div align="center"> | |
| <img src="https://raw.githubusercontent.com/open-mmlab/mmocr/main/resources/textdet.jpg"/><br> | |
| </div> | |
| <br> | |
| The design of text detectors is similar to [SingleStageDetector](https://mmdetection.readthedocs.io/en/latest/api.html#mmdet.models.detectors.SingleStageDetector) in MMDetection. The feature of an image was first extracted by `backbone` (e.g., ResNet), and `neck` further processes raw features into a head-ready format, where the models in MMOCR usually adapt the variants of FPN to extract finer-grained multi-level features. `bbox_head` is the core of text detectors, and its implementation varies in different models. | |
| When training, the output of `bbox_head` is directly fed into the `loss` module, which compares the output with the ground truth and generates a loss dictionary for optimizer's use. When testing, `Postprocessor` converts the outputs from `bbox_head` to bounding boxes, which will be used for evaluation metrics (e.g., hmean-iou) and visualization. | |
| ### DBNet | |
| - Backbone: [mmdet.ResNet](https://mmdetection.readthedocs.io/en/latest/api.html#mmdet.models.backbones.ResNet) | |
| - Neck: [FPNC](https://mmocr.readthedocs.io/en/latest/api.html#mmocr.models.textdet.necks.FPNC) | |
| - Bbox_head: [DBHead](https://mmocr.readthedocs.io/en/latest/api.html#mmocr.models.textdet.dense_heads.DBHead) | |
| - Loss: [DBLoss](https://mmocr.readthedocs.io/en/latest/api.html#mmocr.models.textdet.losses.DBLoss) | |
| - Postprocessor: [DBPostprocessor](https://mmocr.readthedocs.io/en/latest/api.html#mmocr.models.textdet.postprocess.DBPostprocessor) | |
| ### DRRG | |
| - Backbone: [mmdet.ResNet](https://mmdetection.readthedocs.io/en/latest/api.html#mmdet.models.backbones.ResNet) | |
| - Neck: [FPN_UNet](https://mmocr.readthedocs.io/en/latest/api.html#mmocr.models.textdet.necks.FPN_UNet) | |
| - Bbox_head: [DRRGHead](https://mmocr.readthedocs.io/en/latest/api.html#mmocr.models.textdet.dense_heads.DRRGHead) | |
| - Loss: [DRRGLoss](https://mmocr.readthedocs.io/en/latest/api.html#mmocr.models.textdet.losses.DRRGLoss) | |
| - Postprocessor: [DRRGPostprocessor](https://mmocr.readthedocs.io/en/latest/api.html#mmocr.models.textdet.postprocess.DRRGPostprocessor) | |
| ### FCENet | |
| - Backbone: [mmdet.ResNet](https://mmdetection.readthedocs.io/en/latest/api.html#mmdet.models.backbones.ResNet) | |
| - Neck: [mmdet.FPN](https://mmdetection.readthedocs.io/en/latest/api.html#mmdet.models.necks.FPN) | |
| - Bbox_head: [FCEHead](https://mmocr.readthedocs.io/en/latest/api.html#mmocr.models.textdet.dense_heads.FCEHead) | |
| - Loss: [FCELoss](https://mmocr.readthedocs.io/en/latest/api.html#mmocr.models.textdet.losses.FCELoss) | |
| - Postprocessor: [FCEPostprocessor](https://mmocr.readthedocs.io/en/latest/api.html#mmocr.models.textdet.postprocess.FCEPostprocessor) | |
| ### Mask R-CNN | |
| We use the same architecture as in MMDetection. See MMDetection's [config documentation](https://mmdetection.readthedocs.io/en/latest/tutorials/config.html#an-example-of-mask-r-cnn) for details. | |
| ### PANet | |
| - Backbone: [mmdet.ResNet](https://mmdetection.readthedocs.io/en/latest/api.html#mmdet.models.backbones.ResNet) | |
| - Neck: [FPEM_FFM](https://mmocr.readthedocs.io/en/latest/api.html#mmocr.models.textdet.necks.FPEM_FFM) | |
| - Bbox_head: [PANHead](https://mmocr.readthedocs.io/en/latest/api.html#mmocr.models.textdet.dense_heads.PANHead) | |
| - Loss: [PANLoss](https://mmocr.readthedocs.io/en/latest/api.html#mmocr.models.textdet.losses.PANLoss) | |
| - Postprocessor: [PANPostprocessor](https://mmocr.readthedocs.io/en/latest/api.html#mmocr.models.textdet.postprocess.PANPostprocessor) | |
| ### PSENet | |
| - Backbone: [mmdet.ResNet](https://mmdetection.readthedocs.io/en/latest/api.html#mmdet.models.backbones.ResNet) | |
| - Neck: [FPNF](https://mmocr.readthedocs.io/en/latest/api.html#mmocr.models.textdet.necks.FPNF) | |
| - Bbox_head: [PSEHead](https://mmocr.readthedocs.io/en/latest/api.html#mmocr.models.textdet.dense_heads.PSEHead) | |
| - Loss: [PSELoss](https://mmocr.readthedocs.io/en/latest/api.html#mmocr.models.textdet.losses.PSELoss) | |
| - Postprocessor: [PSEPostprocessor](https://mmocr.readthedocs.io/en/latest/api.html#mmocr.models.textdet.postprocess.PSEPostprocessor) | |
| ### Textsnake | |
| - Backbone: [mmdet.ResNet](https://mmdetection.readthedocs.io/en/latest/api.html#mmdet.models.backbones.ResNet) | |
| - Neck: [FPN_UNet](https://mmocr.readthedocs.io/en/latest/api.html#mmocr.models.textdet.necks.FPN_UNet) | |
| - Bbox_head: [TextSnakeHead](https://mmocr.readthedocs.io/en/latest/api.html#mmocr.models.textdet.dense_heads.TextSnakeHead) | |
| - Loss: [TextSnakeLoss](https://mmocr.readthedocs.io/en/latest/api.html#mmocr.models.textdet.losses.TextSnakeLoss) | |
| - Postprocessor: [TextSnakePostprocessor](https://mmocr.readthedocs.io/en/latest/api.html#mmocr.models.textdet.postprocess.TextSnakePostprocessor) | |
| ## Text Recognition Models | |
| **Most of** the implemented recognizers use the following architecture: | |
| <div align="center"> | |
| <img src="https://raw.githubusercontent.com/open-mmlab/mmocr/main/resources/textrecog.jpg"/><br> | |
| </div> | |
| <br> | |
| `preprocessor` refers to any network that processes images before they are fed to `backbone`. `encoder` encodes images features into a hidden vector, which is then transcribed into text tokens by `decoder`. | |
| The architecture diverges at training and test phases. The loss module returns a dictionary during training. In testing, `converter` is invoked to convert raw features into texts, which are wrapped into a dictionary together with confidence scores. Users can access the dictionary with the `text` and `score` keys to query the recognition result. | |
| ### ABINet | |
| - Preprocessor: None | |
| - Backbone: [ResNetABI](https://mmocr.readthedocs.io/en/latest/api.html#mmocr.models.textrecog.backbones.ResNetABI) | |
| - Encoder: [ABIVisionModel](https://mmocr.readthedocs.io/en/latest/api.html#mmocr.models.textrecog.encoders.ABIVisionModel) | |
| - Decoder: [ABIVisionDecoder](https://mmocr.readthedocs.io/en/latest/api.html#mmocr.models.textrecog.decoders.ABIVisionDecoder) | |
| - Fuser: [ABIFuser](https://mmocr.readthedocs.io/en/latest/api.html#mmocr.models.textrecog.fusers.ABIFuser) | |
| - Loss: [ABILoss](https://mmocr.readthedocs.io/en/latest/api.html#mmocr.models.textrecog.losses.ABILoss) | |
| - Converter: [ABIConvertor](https://mmocr.readthedocs.io/en/latest/api.html#mmocr.models.textrecog.convertors.ABIConvertor) | |
| :::{note} | |
| Fuser fuses the feature output from encoder and decoder before generating the final text outputs and computing the loss in full ABINet. | |
| ::: | |
| ### CRNN | |
| - Preprocessor: None | |
| - Backbone: [VeryDeepVgg](https://mmocr.readthedocs.io/en/latest/api.html#mmocr.models.textrecog.backbones.VeryDeepVgg) | |
| - Encoder: None | |
| - Decoder: [CRNNDecoder](https://mmocr.readthedocs.io/en/latest/api.html#mmocr.models.textrecog.decoders.CRNNDecoder) | |
| - Loss: [CTCLoss](https://mmocr.readthedocs.io/en/latest/api.html#mmocr.models.textrecog.losses.CTCLoss) | |
| - Converter: [CTCConvertor](https://mmocr.readthedocs.io/en/latest/api.html#mmocr.models.textrecog.convertors.CTCConvertor) | |
| ### CRNN with TPS-based STN | |
| - Preprocessor: [TPSPreprocessor](https://mmocr.readthedocs.io/en/latest/api.html#mmocr.models.textrecog.preprocessor.TPSPreprocessor) | |
| - Backbone: [VeryDeepVgg](https://mmocr.readthedocs.io/en/latest/api.html#mmocr.models.textrecog.backbones.VeryDeepVgg) | |
| - Encoder: None | |
| - Decoder: [CRNNDecoder](https://mmocr.readthedocs.io/en/latest/api.html#mmocr.models.textrecog.decoders.CRNNDecoder) | |
| - Loss: [CTCLoss](https://mmocr.readthedocs.io/en/latest/api.html#mmocr.models.textrecog.losses.CTCLoss) | |
| - Converter: [CTCConvertor](https://mmocr.readthedocs.io/en/latest/api.html#mmocr.models.textrecog.convertors.CTCConvertor) | |
| ### NRTR | |
| - Preprocessor: None | |
| - Backbone: [ResNet31OCR](https://mmocr.readthedocs.io/en/latest/api.html#mmocr.models.textrecog.backbones.ResNet31OCR) | |
| - Encoder: [NRTREncoder](https://mmocr.readthedocs.io/en/latest/api.html#mmocr.models.textrecog.encoders.NRTREncoder) | |
| - Decoder: [NRTRDecoder](https://mmocr.readthedocs.io/en/latest/api.html#mmocr.models.textrecog.decoders.NRTRDecoder) | |
| - Loss: [TFLoss](https://mmocr.readthedocs.io/en/latest/api.html#mmocr.models.textrecog.losses.TFLoss) | |
| - Converter: [AttnConvertor](https://mmocr.readthedocs.io/en/latest/api.html#mmocr.models.textrecog.convertors.AttnConvertor) | |
| ### RobustScanner | |
| - Preprocessor: None | |
| - Backbone: [ResNet31OCR](https://mmocr.readthedocs.io/en/latest/api.html#mmocr.models.textrecog.backbones.ResNet31OCR) | |
| - Encoder: [ChannelReductionEncoder](https://mmocr.readthedocs.io/en/latest/api.html#mmocr.models.textrecog.encoders.ChannelReductionEncoder) | |
| - Decoder: [ChannelReductionEncoder](https://mmocr.readthedocs.io/en/latest/api.html#mmocr.models.textrecog.decoders.RobustScannerDecoder) | |
| - Loss: [SARLoss](https://mmocr.readthedocs.io/en/latest/api.html#mmocr.models.textrecog.losses.SARLoss) | |
| - Converter: [AttnConvertor](https://mmocr.readthedocs.io/en/latest/api.html#mmocr.models.textrecog.convertors.AttnConvertor) | |
| ### SAR | |
| - Preprocessor: None | |
| - Backbone: [ResNet31OCR](https://mmocr.readthedocs.io/en/latest/api.html#mmocr.models.textrecog.backbones.ResNet31OCR) | |
| - Encoder: [SAREncoder](https://mmocr.readthedocs.io/en/latest/api.html#mmocr.models.textrecog.encoders.SAREncoder) | |
| - Decoder: [ParallelSARDecoder](https://mmocr.readthedocs.io/en/latest/api.html#mmocr.models.textrecog.decoders.ParallelSARDecoder) | |
| - Loss: [SARLoss](https://mmocr.readthedocs.io/en/latest/api.html#mmocr.models.textrecog.losses.SARLoss) | |
| - Converter: [AttnConvertor](https://mmocr.readthedocs.io/en/latest/api.html#mmocr.models.textrecog.convertors.AttnConvertor) | |
| ### SATRN | |
| - Preprocessor: None | |
| - Backbone: [ShallowCNN](https://mmocr.readthedocs.io/en/latest/api.html#mmocr.models.textrecog.backbones.ShallowCNN) | |
| - Encoder: [SatrnEncoder](https://mmocr.readthedocs.io/en/latest/api.html#mmocr.models.textrecog.encoders.SatrnEncoder) | |
| - Decoder: [NRTRDecoder](https://mmocr.readthedocs.io/en/latest/api.html#mmocr.models.textrecog.decoders.NRTRDecoder) | |
| - Loss: [TFLoss](https://mmocr.readthedocs.io/en/latest/api.html#mmocr.models.textrecog.losses.TFLoss) | |
| - Converter: [AttnConvertor](https://mmocr.readthedocs.io/en/latest/api.html#mmocr.models.textrecog.convertors.AttnConvertor) | |
| ### SegOCR | |
| - Backbone: [ResNet31OCR](https://mmocr.readthedocs.io/en/latest/api.html#mmocr.models.textrecog.backbones.ResNet31OCR) | |
| - Neck: [FPNOCR](https://mmocr.readthedocs.io/en/latest/api.html#mmocr.models.textrecog.necks.FPNOCR) | |
| - Head: [SegHead](https://mmocr.readthedocs.io/en/latest/api.html#mmocr.models.textrecog.heads.SegHead) | |
| - Loss: [SegLoss](https://mmocr.readthedocs.io/en/latest/api.html#mmocr.models.textrecog.losses.SegLoss) | |
| - Converter: [SegConvertor](https://mmocr.readthedocs.io/en/latest/api.html#mmocr.models.textrecog.convertors.SegConvertor) | |
| :::{note} | |
| SegOCR's architecture is an exception - it is closer to text detection models. | |
| ::: | |
| ## Key Information Extraction Models | |
| <div align="center"> | |
| <img src="https://raw.githubusercontent.com/open-mmlab/mmocr/main/resources/kie.jpg"/><br> | |
| </div> | |
| <br> | |
| The architecture of key information extraction (KIE) models is similar to text detection models, except for the extra feature extractor. As a downstream task of OCR, KIE models are required to run with bounding box annotations indicating the locations of text instances, from which an ROI extractor extracts the cropped features for `bbox_head` to discover relations among them. | |
| The output containing edges and nodes information from `bbox_head` is sufficient for test and inference. Computation of loss also relies on such information. | |
| ### SDMGR | |
| - Backbone: [UNet](https://mmocr.readthedocs.io/en/latest/api.html#mmocr.models.common.backbones.UNet) | |
| - Neck: None | |
| - Extractor: [mmdet.SingleRoIExtractor](https://mmdetection.readthedocs.io/en/latest/api.html#mmdet.models.roi_heads.SingleRoIExtractor) | |
| - Bbox_head: [SDMGRHead](https://mmocr.readthedocs.io/en/latest/api.html#mmocr.models.kie.heads.SDMGRHead) | |
| - Loss: [SDMGRLoss](https://mmocr.readthedocs.io/en/latest/api.html#mmocr.models.kie.losses.SDMGRLoss) | |