Create handwriting.py

handwriting.py

@@ -0,0 +1,75 @@
+import struct
+
+import numpy as np
+
+import datasets
+from datasets.tasks import ImageClassification
+
+
+_CITATION = """\
+@article{lecun2010mnist,
+  title={MNIST handwritten digit database},
+  author={LeCun, Yann and Cortes, Corinna and Burges, CJ},
+  journal={ATT Labs [Online]. Available: http://yann.lecun.com/exdb/mnist},
+  volume={2},
+  year={2010}
+}
+"""
+
+_DESCRIPTION = """\
+The MNIST dataset consists of 70,000 28x28 black-and-white images in 10 classes (one for each digits), with 7,000
+images per class. There are 60,000 training images and 10,000 test images.
+"""
+_URL = "https://huggingface.co/datasets/AnaChikashua/handwriting/resolve/main/handwriting_dataset.zip"
+_NAMES = ['ა', 'ბ', 'გ', 'დ', 'ე', 'ვ', 'ზ', 'თ', 'ი', 'კ', 'ლ', 'მ', 'ნ', 'ო', 'პ', 'ჟ', 'რ', 'ს', 'ტ', 'უ', 'ფ', 'ქ', 'ღ', 'ყ', 'შ', 'ჩ', 'ც', 'ძ', 'წ', 'ჭ', 'ხ', 'ჯ', 'ჰ']
+
+
+class MNIST(datasets.GeneratorBasedBuilder):
+    """MNIST Data Set"""
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="data",
+            version=datasets.Version("1.0.0"),
+            description=_DESCRIPTION,
+        )
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "image": datasets.Image(),
+                    "label": datasets.features.ClassLabel(names=_NAMES),
+                }
+            ),
+            supervised_keys=("image", "label"),
+            citation=_CITATION,
+            task_templates=[
+                ImageClassification(
+                    image_column="image",
+                    label_column="label",
+                )
+            ],
+        )
+
+    def _split_generators(self, dl_manager):
+        path = dl_manager.dowload(_URL)
+        image_iters = dl_manager.iter_archive(path)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"images": image_iters}
+            ),
+        ]
+
+    def _generate_examples(self, images):
+        """This function returns the examples in the raw form."""
+        for idx, filepath, image in enumerate(images):
+            # extract the text from the filename
+            text = [c for c in str(filepath) if not 0 <= ord(c) <= 127][0]
+            yield idx, {
+                "label": text,
+                "image": {"path": filepath, "bytes": image.read()}
+            }