Phakir Mohan Senapati (1843Senapati ( -1918 was a versatile genius of modern Oriya literature and also the father of Oriya autobiography. His autobiography 'a:tmaji:bancarita' has been translated by two different translators into English. It was John Boulton of the School of Oriental and African Studies, London, who first translated it as 'My Times and I' in 1985. Later Jatindra K. Nayak and Prodeepta Das have translated it again with the title 'Story of My Life ' in 1997. But what is surprising is that Nayak and Das have not even written a foreword to their translation when it is expected of them to state as to why they undertook the task of translating the book again when a translation was already available. So we thought it would be a fruitful exercise to compare, review, and conduct a readability test which would evaluate both the translations."Why is it that each generation (as George Steiner points out) retranslates the works of classical writers? It cannot be only for profit or prestige. It is surely because each age is dissatisfied with the translations of the previous age. But even efforts to 'update' old works, to give them a 'modern flavour', often fall flat." (Duff 1981: 63)

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Deep machine learning includes a series of layers to mimic the working of the human brain for taking a decision. Deep learning networks have shown good results in character recognition in the past. This paper evaluates the performance of different deep learning networks like Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) based recurrent neural network and Convolutional LSTM on printed Odia characters. The Odia character database contains more than 24,000 images of printed Odia characters (including simple as well as complex characters) out of which 23,857 nos. of images are chosen for this work. Besides these three, a nested Convolutional neural network model is developed for different categories of printed character image groups which are formed based on their writing style. Here, in this study, the nested model is showing the best results in terms of error rate, accuracy and no. of epochs in comparison to the other three. Different pre-processing steps like binarization, sizenormalization, blurring, interpolation, etc. are involved before passing the images to the deep neural networks to increase the recognition accuracy.

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