In pattern recognition, identifying Kannada handwritten numerals are a complex knot. This paper portrays an avenue that pikes us to attain a most potent Kannada Numerals recognition process. In this, handwritten Kannada characters are captivated in document fashion and are subjected to Pre-processing and attribute extraction processes. Pre-processing entails steps like noise removal, binarization, normalization, skew amendment, and thinning. Features are extricated by exploiting strategies like Drift Length Count, Direction related progression code, DWT and Curvelet Transfiguration Wrapping. For an impressive classification process deep convolution neural network classifier is preferred. Isolation accuracy of Kannada numeral aimed here will outsource 96% of accuracy.

中文翻译：

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使用深度学习卷积神经网络（DCNN）分类器的手写卡纳达语数字识别

在模式识别中，识别卡纳达语手写数字是一个复杂的结。本文描绘了一条使我们达到最有效的卡纳达语数字识别过程的途径。在这种情况下，手写的卡纳达语字符以文档的方式被迷住了，并经过预处理和属性提取过程。预处理需要采取一些步骤，例如噪声消除，二值化，归一化，偏斜修正和细化。通过利用诸如漂移长度计数，与方向相关的进度代码，DWT和Curvelet变形包装等策略来解开功能。对于令人印象深刻的分类过程，首选深度卷积神经网络分类器。本文针对的Kannada数字隔离精度将外包96％的精度。