TWI706336B - Image processing device and method for detecting and filtering text object - Google Patents
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Abstract
Description
本發明是有關於一種影像處理裝置及其偵測與過濾文字物件的方法,且特別是有關於一種可找出包括文字物件的邊界框的影像處理裝置及其偵測與過濾文字物件的方法。 The present invention relates to an image processing device and a method for detecting and filtering text objects, and more particularly to an image processing device capable of finding a bounding box including text objects and a method for detecting and filtering text objects.
在文字辨識的領域裡,將包括文字的物件從圖像中清楚且正確地擷取並進行辨識,實為至關重要的議題。若能夠精準地在影像中找出包括文字的物件,將可有效地提升其中文字的辨識率。然而,圖像中往往存在許多干擾元素,如不同的光線強弱或複雜的背景,而這些元素常常會增加文字物件辨識的困難度。 In the field of text recognition, it is a crucial issue to clearly and accurately extract and recognize objects including text from images. If an object including text can be accurately found in the image, the recognition rate of the text can be effectively improved. However, there are often many interference elements in the image, such as different light intensity or complex background, and these elements often increase the difficulty of identifying text objects.
有鑑於此,本發明提出一種影像處理裝置及其偵測與過濾文字物件的方法,其可用以解決以上技術問題。 In view of this, the present invention provides an image processing device and a method for detecting and filtering text objects, which can solve the above technical problems.
本發明提供一種偵測與過濾文字物件的方法,包括:取得一影像,其中影像包括一文字物件;分析影像以在影像中找出 多個候選邊界框,其中前述候選邊界框彼此部分地重疊,且各候選邊界框包括一中間區域、一第一側區域、相對於第一側區域的一第二側區域、一第三側區域及相對於第三側區域的一第四側區域;計算各候選邊界框的一分數;依據各候選邊界框的分數從前述候選邊界框中找出包括文字物件的一特定候選邊界框。計算各候選邊界框的分數的步驟包括:對前述候選邊界框中的一第一候選邊界框而言,計算第一候選邊界框的第一側區域的邊緣密度及第二側區域的邊緣密度之間的一邊緣密度比值;計算第一候選邊界框的第一側區域的灰度及第二側區域的灰度之間的一第一灰度差值,並計算第一灰度差值相對於中間區域、第三側區域及第四側區域的灰度的一第一灰度比值;計算第一候選邊界框第三側區域的灰度與第四側灰度區域的灰度之間的一第二灰度差值,並正規化第二灰度差值以得到一灰度差正規化值;將邊緣密度比值、第一灰度比值及灰度差正規化值個別乘以一第一權重值、一第二權重值及一第三權重值並加總,以得到第一候選邊界框的分數。 The present invention provides a method for detecting and filtering text objects, including: obtaining an image, wherein the image includes a text object; analyzing the image to find out in the image A plurality of candidate bounding boxes, where the aforementioned candidate bounding boxes partially overlap each other, and each candidate bounding box includes a middle area, a first side area, a second side area relative to the first side area, and a third side area And a fourth side region relative to the third side region; calculating a score of each candidate bounding box; finding a specific candidate bounding box including the text object from the aforementioned candidate bounding box according to the score of each candidate bounding box. The step of calculating the score of each candidate bounding box includes: for a first candidate bounding box in the aforementioned candidate bounding box, calculating the edge density of the first side area of the first candidate bounding box and the edge density of the second side area Calculate a first gray level difference between the gray level of the first side area of the first candidate bounding box and the gray level of the second side area, and calculate the first gray level difference relative to A first gray scale ratio of the gray levels of the middle area, the third side area, and the fourth side area; calculate the one between the gray level of the third side area of the first candidate bounding box and the gray level of the fourth side gray area The second gray level difference value, and the second gray level difference value is normalized to obtain a gray level difference normalized value; the edge density ratio, the first gray level ratio and the gray level difference normalized value are respectively multiplied by a first weight Value, a second weight value, and a third weight value are added together to obtain the score of the first candidate bounding box.
本發明提供一種影像處理裝置,包括儲存電路及處理器。儲存電路儲存多個模組。處理器耦接儲存電路,存取前述模組以執行下列步驟:取得一影像,其中影像包括一文字物件;分析影像以在影像中找出多個候選邊界框,其中前述候選邊界框彼此部分地重疊,且各候選邊界框包括一中間區域、一第一側區域、相對於第一側區域的一第二側區域、一第三側區域及相對於第三 側區域的一第四側區域;計算各候選邊界框的一分數;依據各候選邊界框的分數從前述候選邊界框中找出包括文字物件的一特定候選邊界框。計算各候選邊界框的分數的步驟包括:對前述候選邊界框中的一第一候選邊界框而言,計算第一候選邊界框的第一側區域的邊緣密度及第二側區域的邊緣密度之間的一邊緣密度比值;計算第一候選邊界框的第一側區域的灰度及第二側區域的灰度之間的一第一灰度差值,並計算第一灰度差值相對於中間區域、第三側區域及第四側區域的灰度的一第一灰度比值;計算第一候選邊界框第三側區域的灰度與第四側灰度區域的灰度之間的一第二灰度差值,並正規化第二灰度差值以得到一灰度差正規化值;將邊緣密度比值、第一灰度比值及灰度差正規化值個別乘以一第一權重值、一第二權重值及一第三權重值並加總,以得到第一候選邊界框的分數。 The invention provides an image processing device, which includes a storage circuit and a processor. The storage circuit stores multiple modules. The processor is coupled to the storage circuit and accesses the aforementioned module to perform the following steps: obtain an image, wherein the image includes a text object; analyze the image to find a plurality of candidate bounding boxes in the image, wherein the candidate bounding boxes partially overlap each other , And each candidate bounding box includes a middle area, a first side area, a second side area relative to the first side area, a third side area, and a third side area relative to the third A fourth side area of the side area; calculating a score of each candidate bounding box; finding a specific candidate bounding box including the text object from the aforementioned candidate bounding box according to the score of each candidate bounding box. The step of calculating the score of each candidate bounding box includes: for a first candidate bounding box in the aforementioned candidate bounding box, calculating the edge density of the first side area of the first candidate bounding box and the edge density of the second side area Calculate a first gray level difference between the gray level of the first side area of the first candidate bounding box and the gray level of the second side area, and calculate the first gray level difference relative to A first gray scale ratio of the gray levels of the middle area, the third side area, and the fourth side area; calculate the one between the gray level of the third side area of the first candidate bounding box and the gray level of the fourth side gray area The second gray level difference value, and the second gray level difference value is normalized to obtain a gray level difference normalized value; the edge density ratio, the first gray level ratio and the gray level difference normalized value are respectively multiplied by a first weight Value, a second weight value, and a third weight value are added together to obtain the score of the first candidate bounding box.
基於上述,本發明提出的影像處理裝置及其偵測與過濾文字物件的方法可在從影像中找出彼此部分重疊的多個候選邊界框之後,計算各候選邊界框的分數,並可將各候選邊界框中具最高分數的一者決定為包括文字物件的特定候選邊界框。藉此,可提升找出文字物件的效率及準確度。 Based on the above, the image processing device and the method for detecting and filtering text objects proposed by the present invention can calculate the score of each candidate bounding box after finding out a plurality of candidate bounding boxes that overlap each other in the image, and can compare each The one with the highest score in the candidate bounding box is determined as the specific candidate bounding box including the text object. In this way, the efficiency and accuracy of finding text objects can be improved.
為讓本發明的上述特徵和優點能更明顯易懂,下文特舉實施例,並配合所附圖式作詳細說明如下。 In order to make the above-mentioned features and advantages of the present invention more comprehensible, the following specific embodiments are described in detail in conjunction with the accompanying drawings.
100:影像處理裝置 100: Image processing device
102:儲存電路 102: storage circuit
104:處理器 104: processor
300、600:影像 300, 600: image
310、610:文字物件 310, 610: text objects
311a、311b、311c、311d、311e、611a:候選邊界框 311a, 311b, 311c, 311d, 311e, 611a: candidate bounding box
411:第一側區域 411: first side area
412:第二側區域 412: second side area
413:第三側區域 413: Third Side Area
414:第四側區域 414: Fourth Side Area
415:中間區域 415: middle area
S210~S240:步驟 S210~S240: steps
圖1是依據本發明之一實施例繪示的影像處理裝置示意圖。 FIG. 1 is a schematic diagram of an image processing device according to an embodiment of the invention.
圖2是依據本發明之一實施例繪示的偵測與過濾文字物件的方法流程圖。 2 is a flowchart of a method for detecting and filtering text objects according to an embodiment of the invention.
圖3是依據本發明之一實施例繪示的影像示意圖。 FIG. 3 is a schematic diagram of an image drawn according to an embodiment of the invention.
圖4是依據圖3繪示的候選邊界框示意圖。 FIG. 4 is a schematic diagram of the candidate bounding box shown in FIG. 3.
圖5是依據圖3繪示的在影像中標示特定候選邊界框的示意圖。 FIG. 5 is a schematic diagram of marking a specific candidate bounding box in an image according to FIG. 3.
圖6是依據本發明之一實施例繪示的文字辨識示意圖。 Fig. 6 is a schematic diagram of character recognition according to an embodiment of the present invention.
請參照圖1,其是依據本發明之一實施例繪示的影像處理裝置示意圖。在本實施例中,影像處理裝置100可以是伺服器、手機、智慧型手機、個人電腦(personal computer,PC)、筆記型電腦(notebook PC)、網本型電腦(netbook PC)、平板電腦(tablet PC)等,但可不限於此。
Please refer to FIG. 1, which is a schematic diagram of an image processing device according to an embodiment of the present invention. In this embodiment, the
如圖1所示,影像處理裝置100包括儲存電路102及處理器104。儲存電路102例如是任意型式的固定式或可移動式隨機存取記憶體(Random Access Memory,RAM)、唯讀記憶體(Read-Only Memory,ROM)、快閃記憶體(Flash memory)、硬碟或其他類似裝置或這些裝置的組合,而可用以記錄多個程式碼或模組。
As shown in FIG. 1, the
處理器104耦接於儲存電路102,並可為一般用途處理器、特殊用途處理器、傳統的處理器、數位訊號處理器、多個微處理器(microprocessor)、一個或多個結合數位訊號處理器核心的微處理器、控制器、微控制器、特殊應用集成電路(Application Specific Integrated Circuit,ASIC)、場可程式閘陣列電路(Field Programmable Gate Array,FPGA)、任何其他種類的積體電路、狀態機、基於進階精簡指令集機器(Advanced RISC Machine,ARM)的處理器以及類似品。
The
在本發明的實施例中,處理器104可載入儲存電路102中所記錄的程式碼或模組以執行本發明提出的偵測與過濾文字物件的方法,以下將作進一步說明。
In the embodiment of the present invention, the
請參照圖2,其是依據本發明之一實施例繪示的偵測與過濾文字物件的方法流程圖。本實施例的方法可由圖1的影像處理裝置100執行,以下即搭配圖1所示的元件來說明圖2各步驟的細節。
Please refer to FIG. 2, which is a flowchart of a method for detecting and filtering text objects according to an embodiment of the present invention. The method of this embodiment can be executed by the
首先,在步驟S210中,處理器104可取得影像。在不同的實施例中,上述影像可以是任意的影像,且其中可包括文字物件,例如招牌、路牌等,但可不限於此。
First, in step S210, the
為便於理解,以下將輔以圖3作具體說明,但其並非用以限定本發明可能的實施方式。 For ease of understanding, the following will be supplemented with FIG. 3 for specific description, but it is not used to limit the possible embodiments of the present invention.
請參照圖3,其是依據本發明之一實施例繪示的影像示意圖。在圖3中,影像300包括文字物件310(其例如是寫著「W.
CHESTNUT ST.」的路牌)。
Please refer to FIG. 3, which is a schematic diagram of an image drawn according to an embodiment of the present invention. In FIG. 3, the
基於圖3,處理器104可接續執行步驟S220以分析影像300以在影像300中找出多個候選邊界框311a、311b、311c、311d、311e。在不同的實施例中,處理器104可基於任何已知的邊緣偵測演算法來在影像300中找出候選邊界框311a~311e。舉例而言,處理器104可先算出影像300中相鄰畫素之間的畫素梯度值。若存在較高的畫素梯度值,即代表該處的畫素存在邊緣,故處理器104可相應地定義出如圖3所示的候選邊界框311a~311e,但本發明可不限於此。
Based on FIG. 3, the
在一實施例中,處理器104亦可先計算影像300的積分影像(Integral Image),並在此積分影像中找出候選邊界框311a~311e,以提升執行的效率。
In one embodiment, the
如圖3所示,候選邊界框311a~311e彼此部分地重疊。相應地,處理器104可從中過濾掉未具體包括文字物件310的一或多者,以找出具體包括文字物件310的特定候選邊界框。
As shown in FIG. 3, the
概略而言,處理器104可執行步驟S230以計算各候選邊界框311a~311e的分數,並執行步驟S240以依據各候選邊界框311a~311e的分數從候選邊界框311a~311e中找出包括文字物件310的特定候選邊界框。相關細節說明如下。
Roughly speaking, the
請參照圖4,其是依據圖3繪示的候選邊界框示意圖。在本實施例中,將以圖3的候選邊界框311a為例來說說明計算分數相關機制,而本領域具通常知識者應可據以推得計算候選邊界框
311b~311e的分數的相關細節。
Please refer to FIG. 4, which is a schematic diagram of the candidate bounding box shown in FIG. 3. In this embodiment, the candidate bounding
如圖4所示,本實施例的候選邊界框311a包括第一側區域411、第二側區域412、第三側區域413、第四側區域414及中間區域415。在本實施例中,第二側區域412的位置相對於第一側區域411,第二側區域412與第一側區域411的長度可與候選邊界框311a相同,第二側區域412與第一側區域411可具有相同的寬度。此外,第三側區域413的位置相對於第四側區域414,且第四側區域414與第三側區域413的長、寬可相同。在決定第一側區域411、第二側區域412、第三側區域413及第四側區域414之後,可將候選邊界框311a中的剩餘區域定義為中間區域415。
As shown in FIG. 4, the candidate bounding
由圖4可看出,第一側區域411、第二側區域412、第三側區域413、第四側區域414及中間區域415彼此可互不重疊,且中間區域415、第三側區域413及第四側區域414介於第一側區域411及第二側區域412之間,但本發明可不限於此。在其他實施例中,設計者亦可依需求而調整圖4中各區塊的長、寬。
It can be seen from FIG. 4 that the
之後,處理器104可計算第一側區域411的邊緣密度及第二側區域412的邊緣密度之間的邊緣密度比值。在本實施例中,第一側區域411的邊緣密度例如是第一側區域411中的多個畫素梯度值的總和。同理,第二側區域412的邊緣密度例如是第二側區域412中的多個畫素梯度值的總和。
After that, the
在一實施例中,在處理器104取得第一側區域411的邊緣密度及第二側區域412的邊緣密度之後,可以第一側區域411
的邊緣密度及第二側區域412的邊緣密度中的較小值除以較大值,以計算上述邊緣密度比值。在本實施例中,上述邊緣密度比值可表徵第一側區域411的邊緣密度與第二側區域412的邊緣密度之間的對稱性。
In an embodiment, after the
接著,處理器104可計算第一側區域411的灰度及第二側區域412的灰度之間的第一灰度差值,並計算第一灰度差值相對於中間區域415、第三側區域413及第四側區域414的灰度的第一灰度比值。
Next, the
具體而言,處理器104可經配置以:計算第一側區域411中的第一平均灰度值;計算第二側區域412中的第二平均灰度值;計算第一平均灰度值及第二平均灰度值的第一絕對差值;計算中間區域415、第三側區域413及第四側區域414的中間平均灰度值,以第一絕對差值除以中間平均灰度值,以產生第二灰度比值;以1減去第二灰度比值,以產生第一灰度比值。在本實施例中,上述中間平均灰度值例如是中間區域415、第三側區域413及第四側區域414的平均灰度值。
Specifically, the
在本實施例中,第一灰度比值可表徵第一側區域311的灰度及第二側區域412的灰度相對於中間區域415、第三側區域413及第四側區域414的灰度的第一灰度對稱性。
In this embodiment, the first gray scale ratio can represent the gray scale of the first side region 311 and the gray scale of the
之後,處理器104可計算第三側區域413的灰度與第四側灰度區域414的灰度之間的第二灰度差值,並正規化第二灰度差值以得到灰度差正規化值。
After that, the
具體而言,處理器104可經配置以:計算第三側區域413中的第三平均灰度值;計算第四側區域414中的第四平均灰度值;計算第三平均灰度值及第四平均灰度值的第二絕對差值;以第二絕對差值除以第三平均灰度值及第四平均灰度值中的一較大值,以產生第三灰度比值;以及以1減去第三灰度比值,以產生上述灰度差正規化值。
Specifically, the
在取得邊緣密度比值、第一灰度比值及灰度差正規化值之後,處理器104可將邊緣密度比值、第一灰度比值及灰度差正規化值個別乘以第一權重值、第二權重值及第三權重值並加總,以得到候選邊界框311a的分數。
After obtaining the edge density ratio, the first grayscale ratio, and the grayscale difference normalized value, the
在一實施例中,第一權重值、第二權重值及第三權重值係基於一遞迴修正過程決定。具體而言,在前述過程中,處理器104可取得一預設影像,並初始化第一權重值、第二權重值及第三權重值,其中影像包括預設文字物件。
In one embodiment, the first weight value, the second weight value, and the third weight value are determined based on a recursive correction process. Specifically, in the foregoing process, the
之後,處理器104可在前述預設影像中找出多個候選邊界框,其中前述候選邊界框中的特定邊界框包括上述預設文字物件。在一實施例中,處理器104可參照先前實施例中教示的方式來在預設影像中找出上述候選邊界框,並計算各候選邊界框的分數。
After that, the
接著,處理器104可遞迴修正第一權重值、第二權重值及第三權重值,直至上述特定邊界框(其包括上述預設文字物件)的分數趨近1且其他邊界框的分數趨近0,且第一權重值、第二權
重值及第三權重值的總和為1。
Then, the
基於以上教示,處理器104可相應地計算圖3中各候選邊界框311a~311e的分數。之後,處理器104可從彼此重疊的候選邊界框311a~311e找出分數最高的一者作為特定候選邊界框(例如候選邊界框311e)。
Based on the above teaching, the
請參照圖5,其是依據圖3繪示的在影像中標示特定候選邊界框的示意圖。承先前實施例中所述,由於候選邊界框311e的分數高於候選邊界框311a~311d,因此處理器104可相應地濾除分數較低的其他候選邊界框311a~311d,以僅在圖5中標示包括文字物件310的候選邊界框311e。
Please refer to FIG. 5, which is a schematic diagram of marking a specific candidate bounding box in an image according to FIG. 3. As described in the previous embodiment, since the score of the
在一實施例中,處理器104還可進一步對候選邊界框311e進行文字辨識,以具體辨識文字物件310中的文字(例如,「W.CHESTNUT ST.」等字樣)。
In one embodiment, the
請參照圖6,其是依據本發明之一實施例繪示的文字辨識示意圖。在本實施例中,可依據先前實施例的教示而在影像600中找出多個候選邊界框,並計算各候選邊界框的分數。之後,可從這些候選邊界框中具最高分數的一者辨識為包括文字物件特定候選邊界框,例如包括文字物件610的候選邊界框611a。並且,還可進一步對包括文字物件610的候選邊界框611a進行文字辨識,以具體辨識文字物件610中的文字(例如,「AB-123」等字樣)。
Please refer to FIG. 6, which is a schematic diagram of character recognition according to an embodiment of the present invention. In this embodiment, a plurality of candidate bounding boxes can be found in the
綜上所述,本發明提出的影像處理裝置及其偵測與過濾文字物件的方法可在從影像中找出彼此部分重疊的多個候選邊界 框之後,基於各候選邊界框的邊緣密度比值、第一灰度比值及灰度差正規化值分數計算各候選邊界框的分數。之後,可將各候選邊界框中具最高分數的一者決定為包括文字物件的特定候選邊界框。藉此,可提升找出文字物件的效率及準確度,進而有助於提升路牌、招牌等辨識機制的效能。 In summary, the image processing device and method for detecting and filtering text objects provided by the present invention can find multiple candidate boundaries that partially overlap each other from the image. After the frame, the score of each candidate bounding box is calculated based on the edge density ratio of each candidate bounding box, the first gray ratio and the normalized value score of the gray difference. After that, the one with the highest score in each candidate bounding box may be determined as the specific candidate bounding box including the text object. In this way, the efficiency and accuracy of finding text objects can be improved, and the efficiency of identification mechanisms such as road signs and signboards can be improved.
雖然本發明已以實施例揭露如上,然其並非用以限定本發明,任何所屬技術領域中具有通常知識者,在不脫離本發明的精神和範圍內,當可作些許的更動與潤飾,故本發明的保護範圍當視後附的申請專利範圍所界定者為準。 Although the present invention has been disclosed in the above embodiments, it is not intended to limit the present invention. Anyone with ordinary knowledge in the technical field can make some changes and modifications without departing from the spirit and scope of the present invention. The scope of protection of the present invention shall be determined by the scope of the attached patent application.
S210~S240:步驟 S210~S240: steps
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Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
TWI408612B (en) * | 2009-09-25 | 2013-09-11 | Ind Tech Res Inst | Method and system for dynamically and simultaneously determining the relative relation between moving objects |
CN104954605A (en) * | 2014-03-31 | 2015-09-30 | 京瓷办公信息系统株式会社 | Image forming apparatus, image forming system, and image forming method |
US9202127B2 (en) * | 2011-07-08 | 2015-12-01 | Qualcomm Incorporated | Parallel processing method and apparatus for determining text information from an image |
CN108304825A (en) * | 2018-02-28 | 2018-07-20 | 北京奇艺世纪科技有限公司 | A kind of Method for text detection and device |
CN108764228A (en) * | 2018-05-28 | 2018-11-06 | 嘉兴善索智能科技有限公司 | Word object detection method in a kind of image |
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Patent Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
TWI408612B (en) * | 2009-09-25 | 2013-09-11 | Ind Tech Res Inst | Method and system for dynamically and simultaneously determining the relative relation between moving objects |
US9202127B2 (en) * | 2011-07-08 | 2015-12-01 | Qualcomm Incorporated | Parallel processing method and apparatus for determining text information from an image |
CN104954605A (en) * | 2014-03-31 | 2015-09-30 | 京瓷办公信息系统株式会社 | Image forming apparatus, image forming system, and image forming method |
CN108304825A (en) * | 2018-02-28 | 2018-07-20 | 北京奇艺世纪科技有限公司 | A kind of Method for text detection and device |
CN108764228A (en) * | 2018-05-28 | 2018-11-06 | 嘉兴善索智能科技有限公司 | Word object detection method in a kind of image |
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