Ancient-Tamil-Script-Recognition

The Ancient Tamil Script is one of the earliest known writing systems evidenced in many parts of India by Epigraphic records found on Rock edicts and Hero stones. Although these inscriptions are a rich source of history, very few applications have been developed to recognize and translate ancient Tamil Script characters. This is mainly due to the lack of proper datasets and very few experts on these long-lost ancient scripts. Furthermore, most stone inscriptions are in conditions that require Image enhancement and Noise removal after the capture of the image. The objective of this project is to develop an architecture to deal with unknown scripts in a systematic way and to curate a dataset necessary for future work. In addition, the aim of this project is to come up with an approach that overcomes the challenges of dealing with stone inscriptions to make it easier to translate them, using Convolutional Neural Networks and other Deep Learning techniques.

Please help to correct this error

Clustering

KMeans = KMeans(number_clusters, random_state=0).fit(featurelist)

ValueError Traceback (most recent call last)
Input In [84], in <cell line: 2>()
1 # Clustering
----> 2 KMeans = KMeans(number_clusters, random_state=0).fit(featurelist)

File ~\anaconda3\lib\site-packages\sklearn\cluster_kmeans.py:1137, in KMeans.fit(self, X, y, sample_weight)
1111 def fit(self, X, y=None, sample_weight=None):
1112 """Compute k-means clustering.
1113
1114 Parameters
(...)
1135 Fitted estimator.
1136 """
-> 1137 X = self._validate_data(
1138 X,
1139 accept_sparse="csr",
1140 dtype=[np.float64, np.float32],
1141 order="C",
1142 copy=self.copy_x,
1143 accept_large_sparse=False,
1144 )
1146 self._check_params(X)
1147 random_state = check_random_state(self.random_state)

File ~\anaconda3\lib\site-packages\sklearn\base.py:566, in BaseEstimator._validate_data(self, X, y, reset, validate_separately, **check_params)
564 raise ValueError("Validation should be done on X, y or both.")
565 elif not no_val_X and no_val_y:
--> 566 X = check_array(X, **check_params)
567 out = X
568 elif no_val_X and not no_val_y:
File ~\anaconda3\lib\site-packages\sklearn\utils\validation.py:769, in check_array(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator)
767 # If input is 1D raise error
768 if array.ndim == 1:
--> 769 raise ValueError(
770 "Expected 2D array, got 1D array instead:\narray={}.\n"
771 "Reshape your data either using array.reshape(-1, 1) if "
772 "your data has a single feature or array.reshape(1, -1) "
773 "if it contains a single sample.".format(array)
774 )
776 # make sure we actually converted to numeric:
777 if dtype_numeric and array.dtype.kind in "OUSV":
ValueError: Expected 2D array, got 1D array instead:
array=[].
Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.

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