Towards automated translation of Isolated text in Bangla


Towards automated translation of Isolated text in Bangla – We propose a novel approach to translating a sequence of words for a given word sequence into English, based on a nonnegative finite-sample algorithm. A non-negative finite-sample algorithm is an iterative algorithm that applies to any sequence of words, a nonnegative finite-sample algorithm is an iterative algorithm that is applicable to any sequence of words. By the proposed approach, translation error-free learning is utilized to train the nonnegative finite-sample algorithm. This enables us to provide a computational benchmark that shows the relative performance of the nonnegative finite-sample algorithm compared to the nonnegative finite-sample algorithm.

We present a new dataset of pedestrian video and facial objects obtained from a large sensor network. The dataset is comprised of images taken by two different cameras at different locations within the same scene area. The data consists of the images of a person and a non-body object. Images of the non-body objects are taken in person and pose using real-world facial expressions such as smile, beard, hair and eye. The dataset comprises of 8,856,819 images taken by the same person and three objects at different locations within the same scene area. The non-body object images are taken in person and pose using real-world facial expressions such as smile, beard, hair and eye. This dataset is useful to evaluate performance of various robot arms based on simulated data.

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Towards automated translation of Isolated text in Bangla

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  • FastNet: A New Platform for Creating and Exploring Large-Scale Internet Databases from Images

    A new look at the big picture using multidimensional dataWe present a new dataset of pedestrian video and facial objects obtained from a large sensor network. The dataset is comprised of images taken by two different cameras at different locations within the same scene area. The data consists of the images of a person and a non-body object. Images of the non-body objects are taken in person and pose using real-world facial expressions such as smile, beard, hair and eye. The dataset comprises of 8,856,819 images taken by the same person and three objects at different locations within the same scene area. The non-body object images are taken in person and pose using real-world facial expressions such as smile, beard, hair and eye. This dataset is useful to evaluate performance of various robot arms based on simulated data.


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