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社区首页 >专栏 >Tesseract-OCR helloworld

Tesseract-OCR helloworld

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vanguard
修改2021-08-03 18:16:48
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修改2021-08-03 18:16:48
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文章被收录于专栏:vanguard

Ubuntu installation

代码语言:shell
复制
sudo apt install tesseract-ocr
pip install pytesseract
# Jetson Nano
# sudo vim ~/.bashrc
# export OPENBLAS_CORETYPE=ARMV8

Python test

代码语言:python
代码运行次数:0
复制
import cv2
import pytesseract
import numpy as np
def ocr_tesseract(path):
    img = cv2.imread(path)
    gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
    gray, img_bin = cv2.threshold(gray,128,255,cv2.THRESH_BINARY | cv2.THRESH_OTSU)
    gray = cv2.bitwise_not(img_bin)
    kernel = np.ones((2, 1), np.uint8)
    img = cv2.erode(gray, kernel, iterations=1)
    img = cv2.dilate(img, kernel, iterations=1)
    return pytesseract.image_to_string(img)
if __name__ == '__main__': print(ocr_tesseract("./test.jpg"))

Windows installation

https://github.com/UB-Mannheim/tesseract/wiki

Github official page

https://github.com/tesseract-ocr/tesseract/

Google cloud

https://cloud.google.com/vision/docs/ocr

中文识别

https://bbs.huaweicloud.com/blogs/143914

test.jpg
test.jpg
代码语言:shell
复制
The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decode
configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple
network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on
two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to
train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including
ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU
score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the
Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data.

[Finished in 2.6s]

原创声明:本文系作者授权腾讯云开发者社区发表,未经许可,不得转载。

如有侵权,请联系 cloudcommunity@tencent.com 删除。

原创声明:本文系作者授权腾讯云开发者社区发表,未经许可,不得转载。

如有侵权,请联系 cloudcommunity@tencent.com 删除。

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