Add the PARSeq model for Scene Text Recognition

keras_hub/api/layers/__init__.py

@@ -52,6 +52,9 @@
 from keras_hub.src.models.pali_gemma.pali_gemma_image_converter import (
     PaliGemmaImageConverter,
 )
+from keras_hub.src.models.parseq.parseq_image_converter import (
+    PARSeqImageConverter,
+)
 from keras_hub.src.models.resnet.resnet_image_converter import (
     ResNetImageConverter,
 )

keras_hub/api/models/__init__.py

@@ -197,6 +197,7 @@
 from keras_hub.src.models.image_object_detector_preprocessor import (
     ImageObjectDetectorPreprocessor,
 )
+from keras_hub.src.models.image_ocr import ImageOCR
 from keras_hub.src.models.image_segmenter import ImageSegmenter
 from keras_hub.src.models.image_segmenter_preprocessor import (
     ImageSegmenterPreprocessor,
@@ -250,6 +251,9 @@
 from keras_hub.src.models.pali_gemma.pali_gemma_tokenizer import (
     PaliGemmaTokenizer,
 )
+from keras_hub.src.models.parseq.parseq_backbone import PARSeqBackbone
+from keras_hub.src.models.parseq.parseq_ocr import PARSeqImageOCR
+from keras_hub.src.models.parseq.parseq_preprocessor import PARSeqPreprocessor
 from keras_hub.src.models.phi3.phi3_backbone import Phi3Backbone
 from keras_hub.src.models.phi3.phi3_causal_lm import Phi3CausalLM
 from keras_hub.src.models.phi3.phi3_causal_lm_preprocessor import (

keras_hub/src/models/image_ocr.py

@@ -0,0 +1,65 @@
+import keras
+
+from keras_hub.src.api_export import keras_hub_export
+from keras_hub.src.models.task import Task
+
+
+@keras_hub_export("keras_hub.models.ImageOCR")
+class ImageOCR(Task):
+    """Base class for all OCR tasks.
+
+    `ImageOCR` tasks wrap a `keras_hub.models.Task` and
+    a `keras_hub.models.Preprocessor` to create a model that can be used for
+    recognizing text in images.
+
+    All `ImageOCR` tasks include a `from_preset()` constructor which can
+    be used to load a pre-trained config and weights.
+    """
+
+    def compile(
+        self,
+        optimizer="auto",
+        loss="auto",
+        *,
+        metrics="auto",
+        **kwargs,
+    ):
+        """Configures the `ImageOCR` task for training.
+
+        The `ImageOCR` task extends the default compilation signature of
+        `keras.Model.compile` with defaults for `optimizer`, `loss`, and
+        `metrics`. To override these defaults, pass any value
+        to these arguments during compilation.
+
+        Args:
+            optimizer: `"auto"`, an optimizer name, or a `keras.Optimizer`
+                instance. Defaults to `"auto"`, which uses the default optimizer
+                for the given model and task. See `keras.Model.compile` and
+                `keras.optimizers` for more info on possible `optimizer` values.
+            loss: `"auto"`, a loss name, or a `keras.losses.Loss` instance.
+                Defaults to `"auto"`, where a
+                `keras.losses.SparseCategoricalCrossentropy` loss will be
+                applied for the classification task. See
+                `keras.Model.compile` and `keras.losses` for more info on
+                possible `loss` values.
+            metrics: `"auto"`, or a list of metrics to be evaluated by
+                the model during training and testing. Defaults to `"auto"`,
+                where a `keras.metrics.SparseCategoricalAccuracy` will be
+                applied to track the accuracy of the model during training.
+                See `keras.Model.compile` and `keras.metrics` for
+                more info on possible `metrics` values.
+            **kwargs: See `keras.Model.compile` for a full list of arguments
+                supported by the compile method.
+        """
+        if optimizer == "auto":
+            optimizer = keras.optimizers.Adam(1e-4)
+        if loss == "auto":
+            loss = keras.losses.SparseCategoricalCrossentropy(from_logits=False)
+        if metrics == "auto":
+            metrics = [keras.metrics.SparseCategoricalAccuracy()]
+        super().compile(
+            optimizer=optimizer,
+            loss=loss,
+            metrics=metrics,
+            **kwargs,
+        )