Someone who waited a long time in line to get tickets. Hope this helps <3
The world has many different religions. Asia has had many religions spring up. Out of these Buddhism and Hinduism are the most popular beliefs in the general population. Hinduism is the oldest known religion and is very rich with literally hundreds of gods, symbolistic rituals and beliefs. It is believed to have been established around 1500 B.C. but one person never founded Hinduism as it evolved over a long period of time. Buddhism on the other hand has a definite founder, Siddhartha Gautama who is otherwise known as the Buddha or Enlightened One who lived from 565 to 483 B.C. Both these religions originated in India. Siddhartha Gautama was a Hindu who found Hindu theology lacking and after years of searching for truth created a religion now known as Buddhism. Because of these basic similarities, the two religions have much in common, but in the same light they differ immensely.
I'm sorry if this is incorrect but this was all I could find :(
In the context End-To-End Measure for Text's crucial to evaluate the performance of text recognition and text line detection engines before comparing systems and configurations. Both jobs have established metrics that can be used independently.
To evaluate the effectiveness of a system that combines text line detection with text recognition, however, there is no sophisticated system in place.
A well-known methodology that is occasionally applied in this context is the F-measure on word level. However, it can produce misleading findings because it does not account for the alignment of the hypothesis and the ground truth text. There is a considerable demand for such a metric since users of automatic information retrieval pipelines in the context of text recognition are primarily interested in the end-to-end performance of a given system.
As a result, we offer a metric to assess a text recognition system's overall quality. The widely accepted character error rate serves as the foundation for this measurement and is only applicable to aligned hypothesis and ground truth texts in its original form. The suggested measure is adaptable in that it may be set up to punish various reading orders between the hypothesis and the actual data as well as take into consideration the text lines' geometric positions. It also has the ability to disregard the over- and under-segmentation of text lines. It is possible to obtain a measure that best suits its own demands using these factors.
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