如何識別 OCR 的多表？

Question

我正在嘗試識別並切割幾張桌子

示范形象

我正在嘗試調整此代碼以識別圖像中最大的表格，但沒有成功

# find contours in the thresholded image and grab the largest one,
# which we will assume is the stats table
cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = imutils.grab_contours(cnts)
tableCnt = max(cnts, key=cv2.contourArea)
# compute the bounding box coordinates of the stats table and extract
# the table from the input image
(x, y, w, h) = cv2.boundingRect(tableCnt)
table = image[y:y + h, x:x + w]

Answer 1

Tesseract是一項具有正確參數的強大技術。 還有一種替代方法是EasyOCR 。 它也可用於光學字符識別。 當我使用您的輸入圖像時：

使用代碼：

import easyocr
reader = easyocr.Reader(['ch_sim','en']) # this needs to run only once to load the model into memory
result = reader.readtext('a.png')
print(result)

我得到了結果：

[([[[269, 5], [397, 5], [397, 21], [269, 21]], '特色產品', 0.9688797744252757), ([[25, 31], [117, 31], [117, 47], [25, 47]], 'Lorem Ipsum', 0.9251252837669294), ([[513, 29], [535, 29], [535, 45], [513, 45]], '1 %', 0.994760876582135), ([[643, 27], [687, 27], [687, 47], [643, 47]], '56.33', 0.9860448082309514), ([[25, 55], [117 , 55], [117, 73], [25, 73]], 'Lorem Ipsum', 0.9625669229848431), ([[505, 55], [543, 55], [543, 71], [505, 71] ], '2.6%', 0.9489194720877449), ([[645, 55], [687, 55], [687, 71], [645, 71]], '59.66', 0.9955955477533281), ([[25, 81] ], [117, 81], [117, 97], [25, 97]], 'Lorem Ipsum', 0.9347195542297398), ([[513, 79], [537, 79], [537, 95], [ 513, 95]], '6%', 0.9802225419827469), ([[643, 77], [687, 77], [687, 97], [643, 97]], '53.55', 0.7060389448443978), ([ [25, 105], [117, 105], [117, 123], [25, 123]], 'Lorem Ipsum', 0.9813030863539253), ([[511, 105], [535, 105], [535, 121], [511, 121]], '2%', 0.96661512341383), ([[643, 105], [687, 105], [687, 121], [643, 121]], '51 .00', 0.9972174551807312), ([[25, 131], [117, 131], [117, 147], [25, 147]], 'Lorem Ipsum', 0.9332194975534566), ([[637, 129], [695, 129], [695, 147], [637, 147]], '$150.00', 0.8416723013481415), ([[23, 155], [115, 155], [115, 173], [23, 173 ]], 'Lorem Ipsum', 0.9628505579362404), ([[619, 155], [711, 155], [711, 171], [619, 171]], '缺貨', 0.5524501407148613), ([[269 , 203], [397, 203], [397, 219], [269, 219]], '特色產品', 0.9892802026085218), ([[25, 227], [117, 227], [117, 245] , [25, 245]], 'Lorem Ipsum', 0.9816736878173294), ([[513, 227], [535, 227], [535, 241], [513, 241]], '1%', 0.7698908738878971) , ([[645, 227], [687, 227], [687, 243], [645, 243]], '56.33', 0.5116652994056308), ([[25, 253], [117, 253], [ 117, 269], [25, 269]], 'Lorem Ipsum', 0.9332997726238675), ([[505, 251], [543, 251], [543, 267], [505, 267]], '2.6% ', 0.5710609510357831), ([[645, 251], [687, 251], [687, 269], [645, 269]], '59.66', 0.9995503012169746), ([[25, 277], [117, 277]、[117、295]、[25、295]]、 'Lorem Ipsum', 0.9626429329615878), ([[513, 277], [537, 277], [537, 293], [513, 293]], '6%', 0.9771388793180815), ([[645, 275] , [687, 275], [687, 293], [645, 293]], '53.55', 0.9578577340198124), ([[269, 313], [397, 313], [397, 329], [269, 329]], '特色產品', 0.9701894261249253), ([[25, 339], [117, 339], [117, 355], [25, 355]], 'Lorem Ipsum', 0.9282643141918978), ([[ 513, 337], [535, 337], [535, 353], [513, 353]], '1%', 0.9946674557074575), ([[643, 335], [687, 335], [687, 355] ], [643, 355]], '56.33', 0.9876496602335217), ([[25, 363], [117, 363], [117, 381], [25, 381]], 'Lorem Ipsum', 0.9625460796304877) , ([[505, 363], [543, 363], [543, 379], [505, 379]], '2.6%', 0.9337789031658965), ([[645, 363], [687, 363], [687, 379], [645, 379]], '59.66', 0.9949654211659896), ([[25, 389], [117, 389], [117, 405], [25, 405]], 'Lorem Ipsum ', 0.931966914707057), ([[513, 387], [537, 387], [537, 403], [513, 403]], '6%', 0.9784907201549085), ([[643, 385], [687 , 385], [687, 405], [643, 405]], '53.55', 0.5365941290893664), ([[25, 413], [117, 413], [117, 431], [25, 431]], 'Lorem Ipsum', 0.980995831244345), ([[511, 413], [535, 413 ], [535, 429], [511, 429]], '2%', 0.9679939124479429), ([[645, 413], [687, 413], [687, 429], [645, 429]], '51.00', 0.9964553415038925), ([[25, 439], [117, 439], [117, 455], [25, 455]], 'Lorem Ipsum', 0.9304503001919713), ([[513, 437], [537, 437], [537, 453], [513, 453]], '6%', 0.9744585914588708), ([[635, 435], [695, 435], [695, 455], [635, 455]], '$150.00', 0.9992132520533294), ([[23, 463], [115, 463], [115, 481], [23, 481]], 'Lorem Ipsum', 0.9626652609420223), ([[619 , 463], [711, 463], [711, 479], [619, 479]], '缺貨', 0.5114405533530642)]

這個結果看起來很復雜，因為它首先給出了檢測到的文本的坐標。 但是，如果您深入研究，您會發現它非常擅長檢測文本。

該視頻還可以幫助您進行安裝。