TWI697844B - Visual artificial intelligence identification method and visual artificial intelligence identification system - Google Patents
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Abstract
本發明係關於一種視覺型人工智慧辨識方法及視覺型人工智慧辨識系統。該視覺型人工智慧辨識方法包括:接收多個待辨識物件之影像資料;對於多個待辨識物件之影像資料,進行人工智慧辨識,以取得人工智慧辨識結果;判斷人工智慧辨識結果,以取得一相對應準確率;依據該相對應準確率,決定是否介入一設定條件至該人工智慧辨識,以重新進行人工智慧辨識;及重複上述人工智慧辨識、判斷人工智慧辨識結果及決定是否介入一設定條件之步驟,以完成多個待辨識物件之辨識。The invention relates to a visual artificial intelligence identification method and a visual artificial intelligence identification system. The visual artificial intelligence identification method includes: receiving image data of multiple objects to be identified; performing artificial intelligence identification on the image data of multiple objects to be identified to obtain an artificial intelligence identification result; judging the artificial intelligence identification result to obtain a Corresponding accuracy rate; based on the corresponding accuracy rate, decide whether to intervene a setting condition to the artificial intelligence recognition to perform artificial intelligence recognition again; and repeat the above artificial intelligence recognition, determine the artificial intelligence recognition result, and decide whether to intervene a setting condition To complete the identification of multiple objects to be identified.
Description
本發明係關於一種視覺型人工智慧辨識方法及視覺型人工智慧辨識系統。The invention relates to a visual artificial intelligence identification method and a visual artificial intelligence identification system.
習知利用人工智慧技術辨識影像資料,係將多個影像資料輸入,以進行人工智慧辨識。若影像資料過多且過於複雜,則人工智慧辨識可能會崩潰,而無法完成辨識;或者,將已完成人工智慧訓練之影像資料,再次輸入,以進行人工智慧辨識,卻得到穩定性更低之結果,造成人工智慧辨識之結果相當不穩定,使得人工智慧辨識的準確率相當低,且習知人工智慧辨識之結果相當不可靠。Xizhi uses artificial intelligence technology to identify image data, and inputs multiple image data for artificial intelligence identification. If the image data is too much and too complex, the artificial intelligence recognition may collapse and the recognition cannot be completed; or, if the image data that has completed the artificial intelligence training is re-input for artificial intelligence recognition, the result will be less stable , The result of artificial intelligence recognition is quite unstable, making the accuracy of artificial intelligence recognition quite low, and the result of conventional artificial intelligence recognition is quite unreliable.
在一實施例中,一種視覺型人工智慧辨識方法包括:接收多個待辨識物件之影像資料;對於多個待辨識物件之影像資料,進行人工智慧辨識,以取得人工智慧辨識結果;判斷人工智慧辨識結果,以取得一相對應準確率;依據該相對應準確率,決定是否介入一設定條件至該人工智慧辨識,以重新進行人工智慧辨識;及重複上述人工智慧辨識、判斷人工智慧辨識結果及決定是否介入一設定條件之步驟,以完成多個待辨識物件之辨識。In one embodiment, a visual artificial intelligence identification method includes: receiving image data of multiple objects to be identified; performing artificial intelligence identification on the image data of multiple objects to be identified to obtain artificial intelligence identification results; and judging artificial intelligence The recognition result to obtain a corresponding accuracy rate; based on the corresponding accuracy rate, decide whether to intervene a setting condition to the artificial intelligence recognition to perform the artificial intelligence recognition again; and repeat the above artificial intelligence recognition and judge the artificial intelligence recognition result and Decide whether to intervene in a step of setting conditions to complete the identification of multiple objects to be identified.
在一實施例中,一種視覺型人工智慧辨識系統包括:一接收裝置、一人工智慧辨識裝置、一判斷裝置及一介入裝置。該接收裝置用以接收多個待辨識物件之影像資料。該人工智慧辨識裝置用以對於多個待辨識物件之影像資料,進行人工智慧辨識,以取得人工智慧辨識結果。該判斷裝置用以判斷人工智慧辨識結果,以取得一相對應準確率。依據該相對應準確率,該介入裝置決定是否介入一設定條件至該人工智慧辨識裝置,以重新進行人工智慧辨識。In one embodiment, a visual artificial intelligence recognition system includes: a receiving device, an artificial intelligence recognition device, a judging device, and an intervention device. The receiving device is used for receiving image data of a plurality of objects to be identified. The artificial intelligence recognition device is used for performing artificial intelligence recognition on the image data of a plurality of objects to be recognized to obtain artificial intelligence recognition results. The judging device is used to judge the artificial intelligence recognition result to obtain a corresponding accuracy rate. According to the corresponding accuracy rate, the intervention device decides whether to intervene a set condition to the artificial intelligence identification device to perform artificial intelligence identification again.
本發明視覺型人工智慧辨識方法及視覺型人工智慧辨識系統可應用於辨識影像資料,例如憑證資料或醫學影像資料等,但不以上述為限。The visual artificial intelligence recognition method and the visual artificial intelligence recognition system of the present invention can be applied to recognize image data, such as credential data or medical image data, but are not limited to the above.
圖1顯示本發明視覺型人工智慧辨識方法之流程圖。圖2顯示本發明視覺型人工智慧辨識系統之方塊示意圖。配合參閱圖1及圖2,請參考步驟S11,接收多個待辨識物件之影像資料。在一實施例中,本發明視覺型人工智慧辨識系統20係利用一接收裝置21,用以接收多個待辨識物件之影像資料。在一實施例中,待辨識物件之影像資料可為圖3A及3B顯示之收銀機二聯式發票之影像資料30、圖4A及4B顯示之統一發票三聯式之影像資料40、圖5A及5B顯示之進貨退出或折讓證明單之影像資料50、圖6A及6B顯示之一電子計算機統一發票之影像資料60或圖7A及7B顯示之中華電信帳單之影像資料70等憑證資料,但不以上述為限。Fig. 1 shows a flowchart of the visual artificial intelligence identification method of the present invention. Figure 2 shows a block diagram of the visual artificial intelligence recognition system of the present invention. With reference to FIG. 1 and FIG. 2, please refer to step S11 to receive image data of multiple objects to be identified. In one embodiment, the visual artificial
配合參閱圖1及圖2,請參考步驟S12,對於多個待辨識物件之影像資料,進行人工智慧辨識,以取得人工智慧辨識結果。在一實施例中,利用一人工智慧辨識裝置22,用以對於多個待辨識物件之影像資料,進行人工智慧辨識,以取得人工智慧辨識結果。在一實施例中,該人工智慧辨識裝置22係用以辨識影像資料,以轉換為所需項目之資料,例如上述憑證之項目資料。With reference to FIGS. 1 and 2, please refer to step S12 to perform artificial intelligence recognition on the image data of multiple objects to be recognized to obtain artificial intelligence recognition results. In one embodiment, an artificial
配合參閱圖1及圖2,請參考步驟S13,判斷人工智慧辨識結果,以取得一相對應準確率。在一實施例中,利用一判斷裝置23,用以判斷人工智慧辨識結果,以取得一相對應準確率。在一實施例中,該相對應準確率包括一相對應成功率及一相對應穩定率。在一實施例中,本發明之視覺型人工智慧辨識系統20之該判斷裝置23包括一比較裝置231,用以比較一設定成功值與該相對應成功率,及比較一設定穩定值及該相對應穩定率。With reference to FIG. 1 and FIG. 2, please refer to step S13 to determine the artificial intelligence recognition result to obtain a corresponding accuracy rate. In one embodiment, a
配合參閱圖1及圖2,請參考步驟S14,依據該相對應準確率,決定是否介入一設定條件至該人工智慧辨識,以重新進行人工智慧辨識。在一實施例中,依據該相對應準確率,利用一介入裝置24,以決定是否介入一設定條件至該人工智慧辨識裝置22,以重新進行人工智慧辨識。在一實施例中,若該相對應準確率不能達到預定的標準,例如:人工智慧辨識結果是崩潰的或失敗的,沒有辨識成功;或者,穩定性不佳,不能達到預定的標準,則需利用該介入裝置24介入該設定條件至該人工智慧辨識裝置22,以重新進行人工智慧辨識,俾提高該相對應準確率。With reference to FIGS. 1 and 2, please refer to step S14 to determine whether to intervene a setting condition to the artificial intelligence recognition according to the corresponding accuracy rate, so as to perform the artificial intelligence recognition again. In one embodiment, according to the corresponding accuracy rate, an
配合參閱圖1及圖2,請參考步驟S15,重複上述人工智慧辨識、判斷人工智慧辨識結果及決定是否介入一設定條件之步驟,以完成多個待辨識物件之辨識。在一實施例中,係利用該人工智慧辨識裝置22、該判斷裝置23及該介入裝置24,進行人工智慧辨識、判斷人工智慧辨識結果及決定是否介入設定條件,俾提高該相對應準確率,並可順利完成多個待辨識物件之辨識。With reference to FIGS. 1 and 2, please refer to step S15 to repeat the steps of artificial intelligence identification, determining the artificial intelligence identification result, and determining whether to intervene in a set condition, to complete the identification of multiple objects to be identified. In one embodiment, the artificial
在一實施例中,由於會計憑證資料之種類及尺寸繁多,且憑證資料項目分布散亂,故若將數目龐大的憑證影像資料作為待辨識物件,輸入至該人工智慧辨識裝置22,在沒有任何設定條件之下,雖然利用人工智慧的方式進行辨識,可能造成人工智慧辨識崩潰的情況,及該相對應準確率不佳的狀況,使得人工智慧辨識結果之可靠度不佳。In one embodiment, due to the various types and sizes of accounting voucher data, and the scattered distribution of voucher data items, if a large number of voucher image data are input to the artificial
圖3A及3B顯示依據本發明之一待辨識物件(收銀機二聯式發票)之影像資料之示意圖。圖4A及4B顯示依據本發明之一待辨識物件(統一發票三聯式)之影像資料之示意圖。圖5A及5B顯示依據本發明之一待辨識物件(進貨退出或折讓證明單)之影像資料之示意圖。圖6A及6B顯示依據本發明之一待辨識物件(電子計算機統一發票)之影像資料之示意圖。圖7A及7B顯示依據本發明之一待辨識物件(中華電信帳單)之影像資料之示意圖。配合參閱圖1、圖2及圖3A至圖7B,該設定條件包括一第一判斷條件,係為該待辨識物件之影像資料之長寬比。依據該待辨識物件之影像資料之長寬比,由於圖3A及3B之待辨識物件(收銀機二聯式發票)之影像資料之長寬比與其他待辨識物件之影像資料之長寬比明顯不同,故可將圖3A及3B之待辨識物件之影像資料辨識歸類為收銀機二聯式發票。3A and 3B show schematic diagrams of image data of an object to be identified (two-part cash register invoice) according to the present invention. 4A and 4B show schematic diagrams of image data of an object to be identified (triple format of unified invoice) according to the present invention. 5A and 5B show schematic diagrams of the image data of an object to be identified (purchase exit or discount certificate) according to the present invention. 6A and 6B show schematic diagrams of image data of an object to be identified (electronic computer unified invoice) according to the present invention. 7A and 7B show schematic diagrams of image data of an object to be identified (Chunghwa Telecom bill) according to the present invention. With reference to FIGS. 1, 2 and 3A to 7B, the setting condition includes a first judgment condition, which is the aspect ratio of the image data of the object to be identified. According to the aspect ratio of the image data of the object to be identified, the aspect ratio of the image data of the object to be identified (cash register dual invoice) in Figures 3A and 3B is obvious compared to the aspect ratio of the image data of other objects to be identified It is different, so the image data identification of the object to be identified in Figures 3A and 3B can be classified as a cash register dual invoice.
在一實施例中,該第一判斷條件另包括該待辨識物件之影像資料之每英吋點數(dpi, dots per inch)。利用該待辨識物件之影像資料之每英吋點數(dpi, dots per inch),亦可辨識得該待辨識物件之影像資料之尺寸,以判斷該待辨識物件之影像資料之種類。In one embodiment, the first judgment condition further includes dots per inch (dpi, dots per inch) of the image data of the object to be identified. Using the dots per inch (dpi, dots per inch) of the image data of the object to be identified, the size of the image data of the object to be identified can also be identified to determine the type of image data of the object to be identified.
依據上述之第一判斷條件,可初步地將具有明顯差異長寬比之該待辨識物件之影像資料辨識出來,然而,由於許多不同的憑證,具有相同尺寸或相同長寬比,例如圖4A及4B之待辨識物件(統一發票三聯式)與圖5A及5B之待辨識物件(進貨退出或折讓證明單)之影像資料之長寬比相同,故利用該第一判斷條件仍不能辨識出圖4A與圖5A之影像資料。例如圖6A及6B之待辨識物件(電子計算機統一發票)與圖7A及7B之待辨識物件(中華電信帳單)之影像資料之長寬比相同,故利用該第一判斷條件仍不能辨識出圖6A與圖7A之影像資料。According to the above-mentioned first judgment condition, the image data of the object to be identified with obviously different aspect ratios can be initially identified. However, because many different certificates have the same size or the same aspect ratio, such as Figures 4A and The aspect ratio of the image data of the object to be identified in 4B (triple unified invoice) is the same as the image data of the object to be identified in Figures 5A and 5B (purchase exit or discount certificate), so the image cannot be identified using the first judgment condition The image data of 4A and Figure 5A. For example, the aspect ratio of the image data of the object to be identified (electronic computer unified invoice) in Figures 6A and 6B is the same as that of the image data of the object to be identified (Chunghwa Telecom bill) in Figures 7A and 7B, so the first judgment condition still cannot be identified The image data of Fig. 6A and Fig. 7A.
在一實施例中,若利用該第一判斷條件及人工智慧辨識,該相對應準確率仍然不佳時,則需再介入設定條件,該設定條件另包括一第二判斷條件,係為設定該待辨識物件之影像資料為多個區域,於該多個區域之至少其中之一,進行人工智慧辨識。在一實施例中,該第二判斷條件為設定該待辨識物件之影像資料為一第一區域、一第二區域及一第三區域。參考圖3A及3B,該待辨識物件(收銀機二聯式發票)之影像資料30設定為一第一區域31、一第二區域32及一第三區域33。於該第一區域31、該第二區域32及該第三區域33之至少其中之一區域,進行人工智慧辨識,以縮小人工智慧辨識區域,降低人工智慧辨識之複雜度,及提高人工智慧辨識之該相對應準確率。In one embodiment, if the first judgment condition and artificial intelligence recognition are used, and the corresponding accuracy rate is still not good, it is necessary to intervene in setting the condition, and the setting condition further includes a second judgment condition, which is to set the The image data of the object to be identified is a plurality of regions, and artificial intelligence identification is performed in at least one of the plurality of regions. In one embodiment, the second judgment condition is to set the image data of the object to be identified as a first area, a second area, and a third area. Referring to FIGS. 3A and 3B, the
在一實施例中,該設定條件包括輸入至少一預定影像資料。參考圖3B,可輸入該預定影像資料34至該人工智慧辨識裝置22。由於收銀機二聯式發票中之該第一區域31會有該預定影像資料34,故若於該待辨識物件之影像資料30之該第一區域31中,可辨識得該預定影像資料34,則可確認該待辨識物件之影像資料30係為收銀機二聯式發票之影像資料,並辨識歸類為收銀機二聯式發票。In one embodiment, the setting condition includes inputting at least one predetermined image data. Referring to FIG. 3B, the
在一實施例中,參考圖4A及4B,該待辨識物件之影像資料40設定為一第一區域41、一第二區域42及一第三區域43。於該第一區域41、該第二區域42及該第三區域43之至少其中之一區域,進行人工智慧辨識,且輸入該預定影像資料(例如:統一發票(三聯式))44至該人工智慧辨識裝置22。由於三聯式統一發票中之該第一區域41會有該預定影像資料44,故若於該待辨識物件之影像資料40之該第一區域41中,可辨識得該預定影像資料44,則可確認該待辨識物件之影像資料40係為三聯式統一發票之影像資料,並辨識歸類為三聯式統一發票。In one embodiment, referring to FIGS. 4A and 4B, the
在一實施例中,參考圖5A及5B,該待辨識物件之影像資料50設定為一第一區域51、一第二區域52及一第三區域53。於該第一區域51、該第二區域52及該第三區域53之至少其中之一區域,進行人工智慧辨識,且輸入該預定影像資料(例如:進貨退出或折讓證明單)54至該人工智慧辨識裝置22。由於進貨退出或折讓證明單中之該第一區域51會有該預定影像資料54,故若於該待辨識物件之影像資料50之該第一區域51中,可辨識得該預定影像資料54,則可確認該待辨識物件之影像資料50係為進貨退出或折讓證明單之影像資料,並辨識歸類為進貨退出或折讓證明單。In one embodiment, referring to FIGS. 5A and 5B, the
在一實施例中,若利用該第一判斷條件、該第二判斷條件及人工智慧辨識,該相對應準確率仍然不佳時,則需再介入設定條件,該設定條件包括一第三判斷條件,係於該多個區域之至少其中之一,設定至少一子區域,於該至少一子區域,進行人工智慧辨識。參考圖6A及6B,該待辨識物件之影像資料60設定為一第一區域61、一第二區域62及一第三區域63,且於該第一區域61設定二子區域611、612,於該至少一子區域,進行人工智慧辨識。並且,輸入該預定影像資料(例如:電子計算機)64至該人工智慧辨識裝置22。由於電子計算機統一發票中之該第一區域41之子區域611會有該預定影像資料64,故若於該待辨識物件之影像資料60之該第一區域61之該子區域611中,可辨識得該預定影像資料64,則可確認該待辨識物件之影像資料60係為電子計算機統一發票之影像資料,並辨識歸類為電子計算機統一發票。In one embodiment, if the first judgment condition, the second judgment condition, and artificial intelligence recognition are used, and the corresponding accuracy is still not good, it is necessary to intervene in setting conditions, and the setting conditions include a third judgment condition , Set at least one sub-area in at least one of the plurality of areas, and perform artificial intelligence recognition in the at least one sub-area. 6A and 6B, the
在一實施例中,若利用該第一判斷條件、該第二判斷條件、該第三判斷條件及人工智慧辨識,該相對應準確率仍然不佳時,則需再介入設定條件,該設定條件包括一第四判斷條件,係於該至少一子區域,設定至少一邊區域,於該至少一邊區域,進行人工智慧辨識。參考圖7A及7B,該待辨識物件之影像資料70設定為一第一區域71、一第二區域72及一第三區域73,且於該第一區域71設定二子區域711、712,並於該子區域711,設定至少一邊區域713,於該至少一邊區域713,進行人工智慧辨識。並且,輸入該預定影像資料(例如:中華電信及其圖樣)74至該人工智慧辨識裝置22。由於中華電信帳單中之該第一區域71之子區域711之該邊區域713會有該預定影像資料74,故若於該待辨識物件之影像資料70之該第一區域71之該子區域711之該邊區域713中,可辨識得該預定影像資料74,則可確認該待辨識物件之影像資料70係為中華電信帳單之影像資料,並辨識歸類為中華電信帳單。In one embodiment, if using the first judgment condition, the second judgment condition, the third judgment condition, and the artificial intelligence identification, if the corresponding accuracy is still not good, it is necessary to intervene in setting the condition again, the setting condition It includes a fourth judgment condition, which is related to the at least one sub-region, at least one side region is set, and artificial intelligence identification is performed in the at least one side region. 7A and 7B, the
利用本發明視覺型人工智慧辨識方法及視覺型人工智慧辨識系統20,可使人工智慧辨識結果之準確率提高,亦即人工智慧辨識結果之成功率提高,且人工智慧辨識結果之穩定率提高,以提昇人工智慧辨識結果之可靠度。並且,利用本發明視覺型人工智慧辨識方法及視覺型人工智慧辨識系統20,可降低人工智慧辨識之複雜度。Using the visual artificial intelligence recognition method and visual artificial
上述實施例僅為說明本發明之原理及其功效,並非限制本發明,因此習於此技術之人士對上述實施例進行修改及變化仍不脫本發明之精神。本發明之權利範圍應如後述之申請專利範圍所列。The above-mentioned embodiments only illustrate the principles and effects of the present invention, and do not limit the present invention. Therefore, those skilled in the art can modify and change the above-mentioned embodiments without departing from the spirit of the present invention. The scope of rights of the present invention should be listed in the scope of patent application described later.
20:視覺型人工智慧辨識系統
21:接收裝置
22:人工智慧辨識裝置
23:判斷裝置
231:比較裝置
24:介入裝置
30:待辨識物件之影像資料
31:第一區域
32:第二區域
33:第三區域
34:預定影像資料
40:待辨識物件之影像資料
41:第一區域
42:第二區域
43:第三區域
44:預定影像資料
50:待辨識物件之影像資料
51:第一區域
52:第二區域
53:第三區域
54:預定影像資料
60:待辨識物件之影像資料
61:第一區域
62:第二區域
63:第三區域
64:預定影像資料
611、612:子區域
70:待辨識物件之影像資料
71:第一區域
72:第二區域
73:第三區域
74:預定影像資料
711、712:子區域
713:邊區域
S11~S15:步驟
20: Visual artificial intelligence recognition system
21: receiving device
22: Artificial intelligence identification device
23: Judging device
231: comparison device
24: Interventional device
30: Image data of the object to be identified
31: The first area
32: second area
33: The third area
34: Scheduled image data
40: Image data of the object to be identified
41: The first area
42: second area
43: The third area
44: Scheduled image data
50: Image data of the object to be identified
51: The first area
52: second area
53: The third area
54: Scheduled image data
60: Image data of the object to be identified
61: The first area
62: second area
63: The third area
64: Scheduled
圖1顯示本發明視覺型人工智慧辨識方法之流程圖。Fig. 1 shows a flowchart of the visual artificial intelligence identification method of the present invention.
圖2顯示本發明視覺型人工智慧辨識系統之方塊示意圖。Figure 2 shows a block diagram of the visual artificial intelligence recognition system of the present invention.
圖3A及3B顯示依據本發明之一待辨識物件(收銀機二聯式發票)之影像資料之示意圖。3A and 3B show schematic diagrams of image data of an object to be identified (two-part cash register invoice) according to the present invention.
圖4A及4B顯示依據本發明之一待辨識物件(統一發票三聯式)之影像資料之示意圖。4A and 4B show schematic diagrams of image data of an object to be identified (triple format of unified invoice) according to the present invention.
圖5A及5B顯示依據本發明之一待辨識物件(進貨退出或折讓證明單)之影像資料之示意圖。5A and 5B show schematic diagrams of the image data of an object to be identified (purchase exit or discount certificate) according to the present invention.
圖6A及6B顯示依據本發明之一待辨識物件(電子計算機統一發票)之影像資料之示意圖。6A and 6B show schematic diagrams of image data of an object to be identified (electronic computer unified invoice) according to the present invention.
圖7A及7B顯示依據本發明之一待辨識物件(中華電信帳單)之影像資料之示意圖。7A and 7B show schematic diagrams of image data of an object to be identified (Chunghwa Telecom bill) according to the present invention.
S11~S15:步驟 S11~S15: steps
Claims (18)
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TW200816097A (en) * | 2006-09-20 | 2008-04-01 | Primax Electronics Ltd | Method for characterizing texture of areas within an image corresponding to monetary banknotes |
CN103761210A (en) * | 2014-01-02 | 2014-04-30 | Tcl集团股份有限公司 | Setting method for threshold values of multiple classifiers |
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TW200816097A (en) * | 2006-09-20 | 2008-04-01 | Primax Electronics Ltd | Method for characterizing texture of areas within an image corresponding to monetary banknotes |
TW201447773A (en) * | 2013-06-13 | 2014-12-16 | Univ Nat Yunlin Sci & Tech | License plate recognition method and the handheld electronic device |
CN103761210A (en) * | 2014-01-02 | 2014-04-30 | Tcl集团股份有限公司 | Setting method for threshold values of multiple classifiers |
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