For analytical approach-based word recognition techniques, the task of segmenting the word into individual characters is a big challenge, specifically for cursive handwriting. For this, a holistic approach can be a better option, wherein the entire word is passed to an appropriate recognizer. Gurumukhi script is a complex script for which a holistic approach can be proposed for offline handwritten word recognition. In this paper, the authors propose a Convolutional Neural Network-based architecture for recognition of the Gurumukhi month names. The architecture is designed with five convolutional layers and three pooling layers. The authors also prepared a dataset of 24,000 images, each with a size of 50 × 50. The dataset was collected from 500 distinct writers of different age groups and professions. The proposed method achieved training and validation accuracies of about 97.03% and 99.50%, respectively for the proposed dataset.
Machine learning is a common application of artificial intelligence, which gives machines the ability to learn from data automatically and improve with experience without being explicitly programmed. Supervised learning is one of two broad areas of machine learning that deal with the task of learning a function for a current model based on past data training on input-output pairs of examples. This function enables the model to predict future outcomes for new inputs. Regression and classification are two supervised machine learning problems. Classification is the most common task that intelligent systems perform most often. This review describes the classification algorithm’s working and its applications to optical character recognition in Indic script. Devanagari and Gurumukhi scripts are chosen for this attempt. The detailed study presented in this article will be helpful to researchers working in the field of optical character recognition to understand where to use which machine learning algorithm for best results.
The Gurumukhi script has a complex structure for which text recognition based on an analytical approach can misinterpret the script. For error-free results in text recognition, the author has proposed a holistic approach based on classi cation of Gurumukhi month's name images. For this, a new convolutional neural model has been developed for automatic feature extraction from Gurumukhi text images. The proposed convolutional neural network is designed with ve convolutional, three polling layers, one atten layer and one dense layer. To validate the results of the proposed model, the dataset was self-created from 500 distinct writers. The performance of the model has been analyzed with 100 epochs, 40 batch sizes and different optimizers. The various optimizers that have been used for this experimentation are SGD, Adagrad, Adadelta, RMSprop, Adam, and Nadam. The experimental results show that the proposed CNN model performed best with Adam optimizer in terms of accuracy, computational time, F1 score, precision and recall.
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