TWM611233U - System for examining financial service application - Google Patents
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說明書提出一種申請金融服務的技術,特別是指應用了以智能方法識別文件內容作為申請金融服務證明文件的金融服務申請審核系統。The manual proposes a technology for applying for financial services, in particular, it refers to a financial service application review system that uses intelligent methods to identify the content of a document as a certification document for applying for financial services.
習知向銀行提出各種貸款申請或信用卡申請時,銀行會向申請人要求財力證明文件,例如存摺、扣繳憑單,以此評估申請人的財力與還款能力,據此決定一信用額度。When applying for various loans or credit cards to the bank, the bank will ask the applicant for financial proof documents, such as passbooks and withholding vouchers, in order to assess the applicant's financial strength and repayment ability, and determine a credit limit accordingly.
舉例來說,在現有技術中,當申請人需要貸款時,需要填寫各種文件,特別是提交上述證明文件,銀行人員即接著審核申請文件與證明文件,這個過程往往耗費人力與時間。For example, in the prior art, when an applicant needs a loan, it needs to fill in various documents, especially when the above-mentioned certification documents are submitted, the bank staff will then review the application documents and the certification documents. This process often consumes manpower and time.
除了臨櫃申請外,現有技術也有提供申請人通過軟體介面上傳相關證明文件,但銀行端仍是人為的審核方式,效率提昇有限。In addition to the counter application, the existing technology also provides applicants to upload relevant certification documents through the software interface, but the bank side is still a manual review method, and the efficiency improvement is limited.
說明書公開一種應用文件內容識別技術的金融服務申請審核系統,其中方法利用智能模型識別文件中的內容,實現自動化申請特定金融服務的目的。The specification discloses a financial service application review system using document content identification technology, in which the method uses an intelligent model to identify the content in the document to achieve the purpose of automatically applying for a specific financial service.
根據實施例,所提出的金融服務申請審核系統包括一伺服系統,設有數據庫,其中儲存各客戶提供的文件,伺服系統運行一金融服務申請審核方法,其中採用了所述的文件內容識別技術。在此文件內容識別技術中,所提出的伺服系統通過一使用者介面接收客戶提供的文件,接著對文件的影像執行一影像辨識方法,根據定義好的多個欄位定位出文件中各欄位中物件涵蓋的多個第一類區域。另外,對文件的影像執行一內容識別方法,以得出文件中字元以及字元之間關聯性,可決定文字或數字的多個第二類區域。之後根據影像中鄰接的第一類區域與第二類區域的相關性決定多筆文字或數字資訊的多個辨識區域,同時排除不須辨識的區域,經識別各辨識區域中的文字或數字,應用在金融服務申請時,即根據此識別結果計算一財力,作為財力證明,據此審核客戶提出的一金融服務申請。According to an embodiment, the proposed financial service application review system includes a server system with a database in which documents provided by customers are stored, and the server system runs a financial service application review method, which uses the document content identification technology. In this document content recognition technology, the proposed server system receives the document provided by the customer through a user interface, and then executes an image recognition method on the image of the document, and locates each field in the document according to multiple defined fields Multiple first-class areas covered by the middle object. In addition, a content recognition method is performed on the image of the document to obtain the characters and the correlation between the characters in the document, and a plurality of second-type areas of characters or numbers can be determined. Then, according to the correlation between the adjacent first type area and the second type area in the image, multiple recognition areas of multiple text or digital information are determined, and areas that do not need to be recognized are excluded. After recognizing the text or numbers in each recognition area, When applied to a financial service application, a financial power is calculated based on the identification result, which serves as a financial power proof to review a financial service application submitted by a customer.
所述文件可以為用以證明客戶財力之證明文件,證明文件為具有多欄位時間與金額資訊的影像檔案。The document may be a certification document used to prove the financial strength of the client, and the certification document is an image file with multiple fields of time and amount information.
為使能更進一步瞭解本新型的特徵及技術內容,請參閱以下有關本新型的詳細說明與圖式,然而所提供的圖式僅用於提供參考與說明,並非用來對本新型加以限制。In order to further understand the features and technical content of the present invention, please refer to the following detailed descriptions and drawings about the present invention. However, the drawings provided are only for reference and explanation, and are not used to limit the present invention.
以下是通過特定的具體實施例來說明本創作的實施方式,本領域技術人員可由本說明書所公開的內容瞭解本創作的優點與效果。本創作可通過其他不同的具體實施例加以施行或應用,本說明書中的各項細節也可基於不同觀點與應用,在不悖離本創作的構思下進行各種修改與變更。另外,本創作的附圖僅為簡單示意說明,並非依實際尺寸的描繪,事先聲明。以下的實施方式將進一步詳細說明本創作的相關技術內容,但所公開的內容並非用以限制本創作的保護範圍。The following is a specific specific embodiment to illustrate the implementation of this creation, and those skilled in the art can understand the advantages and effects of this creation from the content disclosed in this specification. This creation can be implemented or applied through other different specific embodiments, and various details in this specification can also be based on different viewpoints and applications, and various modifications and changes can be made without departing from the concept of this creation. In addition, the drawings of this creation are merely schematic illustrations, and are not depicted in actual size, and are stated in advance. The following implementations will further describe the related technical content of this creation in detail, but the disclosed content is not intended to limit the protection scope of this creation.
應當可以理解的是,雖然本文中可能會使用到“第一”、“第二”、“第三”等術語來描述各種元件或者信號,但這些元件或者信號不應受這些術語的限制。這些術語主要是用以區分一元件與另一元件,或者一信號與另一信號。另外,本文中所使用的術語“或”,應視實際情況可能包括相關聯的列出項目中的任一個或者多個的組合。It should be understood that although terms such as "first", "second", and "third" may be used herein to describe various elements or signals, these elements or signals should not be limited by these terms. These terms are mainly used to distinguish one element from another, or one signal from another signal. In addition, the term "or" used in this document may include any one or a combination of more of the associated listed items depending on the actual situation.
揭露書揭示一種應用文件內容識別技術的金融服務申請審核系統,所提出的系統的主要目的之一是能夠以智能與自動化程序讓系統(如金融機構)快速地審核客戶提出的特定服務請求,例如金融機構提供的信用卡申請、貸款申請服務。The disclosure reveals a financial service application review system that uses document content recognition technology. One of the main purposes of the proposed system is to enable systems (such as financial institutions) to quickly review customer-specific service requests with intelligent and automated procedures, such as Credit card application and loan application services provided by financial institutions.
圖1顯示為金融服務申請審核系統架構實施例圖,其中金融服務申請審核系統採用揭露書提出的文件內容識別技術,其中主要是通過軟體方法實現的人工智能技術,識別出文件中的資訊,作為金融服務申請審核的用途。Figure 1 shows an example of the structure of the financial service application review system. The financial service application review system uses the document content recognition technology proposed in the disclosure. The artificial intelligence technology realized by the software method is mainly used to identify the information in the document. The purpose of financial service application review.
圖例顯示金融服務申請審核系統設有一伺服系統11,其中以軟體手段與電腦主機等硬體搭配實現各種系統端的功能,例如執行影像模糊判斷的模糊判斷模組111,這是為在影像辨識方法之前的前處理程序,以影像處理技術判斷所接收的文件(申請文件或證明文件)的影像是否合格;還包括分類客戶提供財力證明的財證分類模組112,主要是能從客戶提供的文件中得出財力證明文件;還包括校正接收的影像使得可以執行後續辨識的影像校正模組113;還提出一以影像處理方法偵測影像中各種物件資訊的物件偵測模組114,這是用以對文件的影像執行一影像辨識方法,根據定義好的多個欄位定位出文件中各欄位中物件所涵蓋的區域,如實施例提出的第一類區域;再提出定位所提出文件(如財力證明文件)其中資訊(如中文、英文等文字、數字)位置的定位修正模組115。The illustration shows that the financial service application review system is equipped with a
更者,系統提出用於辨識其中文字與數字內容的文數字辨識模組116,其中採用內容識別方法,能對文件的影像進行內容識別,可得出文件中字元以及字元之間關聯性,決定文字或數字的區域,如實施例提出的第二類區域,此內容識別方法繼續根據鄰接的第一類區域與第二類區域的相關性決定多筆文字或數字資訊的多個辨識區域,同時排除不須辨識的區域,以識別文件中多個辨識區域中的文字或數字。系統還提出可以根據文件預設的格式以針對辨識得到的內容驗證其正確性的驗證修正模組117,以及對所識別得出的金額計算財力(提供金融服務申請審核系統審核金融服務的申請)的財力計算模組118等。伺服系統11設有數據庫13,用以儲存各客戶提供的文件,如針對特定金融服務的申請文件與證明文件。Furthermore, the system proposes a text and
系統提供客戶使用各式客戶端裝置101, 103以網頁或特定軟體程式等起始的使用者介面產生申請文件與證明文件的電子檔案,並通過網路10上傳到伺服系統11。另外,金融從業人員更可透過金融服務裝置105,掃描相關申請文件與財力證明文件,通過網路10傳送到伺服系統11,再由上述各功能模組處理得出其中財力資訊。The system provides customers using
根據一實施範例,但此範例並非用以限制所提出的創作,以上以軟體與硬體搭配實現的功能模組各有特定手段與方法,例如模糊判斷模組111可採用拉普拉斯方差;財證分類模組112可採用習知的Resnet 50模型;影像校正模組113可採用揭露書提出的文件內容識別方法中的第一智能模型(例如:CRAFT);物件偵測模組114應用的演算法可以為文件內容識別方法中採用的第一智能模型與第二智能模型之其中之一(例如為CRAFT和Yolo其中之一);文數字辨識模組116可採用的演算技術可以是習知的crnn+ctc、attention類模型之其中之一;財力計算模組118可運用欄位值映射、財力值邏輯運算之其中之一。According to an implementation example, this example is not intended to limit the proposed creation. The above functional modules implemented with software and hardware have specific means and methods. For example, the
伺服系統11可實現金融機構中的伺服器,利用伺服器運行網路銀行或行動銀行提供客戶遠端以網頁或行動裝置應用程式(APP)申請特定金融服務(如信用卡、貸款),以能自動化處理相關文件而完成申請流程。揭露書所提出文件內容識別技術適用各種文件影像,申請文件如個人身份證明文件,證明文件則可為各式財力證明文件。The
圖2顯示運行於伺服系統中的金融服務申請審核方法的實施例流程圖。Fig. 2 shows a flowchart of an embodiment of a financial service application review method running in a server system.
當用戶於終端操作上述實施例所提到的客戶端裝置或自助式金融服務裝置提出申請某項金融服務的請求,通過網路傳送到伺服系統,即由伺服系統接收此金融服務的請求(步驟S201),這時,伺服系統即啟動一處理流程,包括傳送信息或是由軟體程式產生信息要求客戶提出相關文件,例如申請某項金融服務的申請文件以及足以證明客戶財力的證明文件,藉此讓系統可以核發一信用額度(步驟S203),此時,客戶可利用客戶端裝置以拍照、掃描或填寫申請文件與產生證明文件的影像,伺服系統即接收這些文件檔案與影像(步驟S205)。When the user operates the client device or self-service financial service device mentioned in the above embodiment on the terminal, the request for a certain financial service is sent to the server system through the network, that is, the server system receives the request for the financial service (step S201). At this time, the server system initiates a processing flow, including sending information or generating information from a software program to request the customer to submit relevant documents, such as an application document for a certain financial service and a certification document sufficient to prove the customer’s financial resources. The system can issue a credit limit (step S203). At this time, the client can use the client device to take photos, scan or fill in the application documents and generate the images of the certification documents, and the server system receives these files and images (step S205).
這時,伺服系統中通過其處理器運行一軟體程序,執行文件內容識別方法(步驟S207),其中涵蓋一些影像處理演算法可參考圖3描述的實施例流程,其主要目的之一是能夠以軟體方法或採用特定智能方法辨識出文件中資訊,在申請金融服務的目的上,目的是能夠取得其中財力資訊(步驟S209),擷取其中英文數字的資訊,進一步可計算客戶財力(步驟S211),根據其中資訊審核客戶提出的金融服務申請(步驟S213)。舉例來說,從客戶提供的文件中計算出客戶財力,評估客戶還款能力,可以據此核可一信用額度。At this time, the servo system runs a software program through its processor to execute the document content recognition method (step S207), which covers some image processing algorithms. Refer to the embodiment flow described in FIG. 3, one of its main purposes is to be able to use software Method or use a specific intelligent method to identify the information in the document. For the purpose of applying for financial services, the purpose is to obtain financial information (step S209), extract the information in English numbers, and further calculate the customer's financial resources (step S211). According to the information therein, the financial service application submitted by the customer is reviewed (step S213). For example, by calculating the financial resources of the customer from the documents provided by the customer and evaluating the customer's repayment ability, a credit limit can be approved accordingly.
為了實現上述技術目的,其中採用了一種文件內容識別方法,如圖3所示的實施例流程圖。In order to achieve the above technical objectives, a method for identifying file content is adopted, as shown in the flowchart of the embodiment shown in FIG. 3.
在此流程實施例中,一開始,客戶於終端產生文件影像,如掃描存摺、扣繳憑單等可證明客戶財力的證明文件,文件也可包括申請文件,一併上傳到伺服系統,經伺服器接收此文件影像後(步驟S301),可以通過影像處理演算法進行前處理程序,如清晰度識別以排除無法處理的影像,還可分類各式文件(步驟S303),並執行影像校正(步驟S305)。In this process embodiment, at the beginning, the customer generates document images at the terminal, such as scanning passbooks, withholding vouchers, and other documents that can prove the customer’s financial strength. The documents can also include application documents, which are uploaded to the server system and passed through the server. After receiving the file image (step S301), image processing algorithms can be used to perform pre-processing procedures, such as sharpness recognition to exclude images that cannot be processed, and various types of files can be classified (step S303), and image correction can be performed (step S305) ).
舉例來說,在影像處理的前處理的程序中,可利用影像處理的技術判斷影像是否合格(是否模糊、歪斜),其中之一方式是根據整張影像的畫素值(灰階值、亮度值等)判斷是否有明確邊界(一般文件的英文數字應有明顯邊界),若畫素值過於均勻,判斷為模糊,即提出重新上傳的信息。若確認文件影像品質合格,需要時,還可執行步驟S305的影像校正流程,包括歪斜校正、梯形校正、色溫校正等。For example, in the pre-processing procedure of image processing, image processing technology can be used to determine whether the image is qualified (whether it is blurred or skewed). One of the methods is based on the pixel value of the entire image (grayscale value, brightness). Value, etc.) to determine whether there is a clear boundary (the English numbers of general documents should have obvious boundaries), if the pixel value is too uniform, it is judged to be blurred, and the information to be re-uploaded is proposed. If it is confirmed that the image quality of the document is qualified, if necessary, the image correction process of step S305 may be executed, including skew correction, keystone correction, color temperature correction, and so on.
接著,在步驟S307中,對文件的影像執行一影像辨識方法,能根據定義好的多個欄位定位出證明文件中各欄位中物件涵蓋的多個第一類區域,其中框選其中物件的圖例可參考圖4所示範例。根據一實施例,文件如財力的證明文件,所述方法將以智能技術定位證明文件中欄位,所述欄位如存摺中的日期、摘要、餘額等,這些是已知定義好的欄位。舉例來說,實施例之一採用了第一智能模型,當以機器學習演算法完成訓練第一智能模型後,可套用在接收到的文件影像上,以在文件的影像中框選出當中文字或數字涵蓋的第一類區域。Then, in step S307, an image recognition method is performed on the image of the document, which can locate multiple first-type areas covered by the objects in each field in the certification document based on the multiple defined fields, and select the objects among them. Refer to the example shown in Figure 4 for the legend. According to an embodiment, the document is a proof of financial resources, and the method will use smart technology to locate the fields in the proof document, such as the date in the passbook, summary, balance, etc., which are known and defined fields . For example, one of the embodiments adopts the first intelligent model. After the machine learning algorithm is used to complete the training of the first intelligent model, it can be applied to the received document image to select Chinese characters or text in the document image. The first type of area covered by the numbers.
在步驟S309中,對所接收到的文件的影像執行一內容識別方法,根據識別當中的字元以及其相關的欄位資訊得出文件中字元以及字元之間關聯性,可以決定文字或數字的多個第二類區域。舉例來說,實施例之一採用第二智能模型,可用以判斷文件的文字或數字熱點區,再根據字元以及字元之間關聯性以在文件的影像中框選出具有文字或數字的第二類區域。In step S309, a content recognition method is performed on the image of the received document. According to the recognized characters and their related field information, the characters in the document and the relationship between the characters can be determined, and the characters or numbers can be determined. Of multiple second-class areas. For example, one of the embodiments adopts the second smart model, which can be used to determine the text or number hotspot of the document, and then according to the characters and the correlation between the characters, the second intelligent model with text or numbers is selected in the image of the document. The second category area.
之後,綜合上述第一、第二智能模型即可得出適當的辨識區域,所述方法將根據文件影像中鄰接的第一類區域與第二類區域之間的相關性決定其中多筆文字或數字資訊的多個辨識區域,同時排除不須辨識的區域(步驟S311),其目的主要是識別文件中多個辨識區域中的文字或數字。可參考圖6A所示範例,所述鄰接的第一類區域與第二類區域的相關性可為相鄰第一類區域與第二類區域聯集的區域面積。After that, the first and second smart models mentioned above can be combined to obtain the appropriate recognition area. The method will determine the number of characters or characters in the document image based on the correlation between the first type area and the second type area adjacent to the document image. The multiple identification areas of the digital information and the areas that do not need to be identified at the same time are excluded (step S311), the purpose of which is mainly to identify the characters or numbers in the multiple identification areas in the document. Referring to the example shown in FIG. 6A, the correlation between the adjacent first-type area and the second-type area may be the area of the adjacent first-type area and the second-type area.
接著,還在另一實施例中,可以執行一些方便後續軟體程序的步驟,如將辨識區域裁切成為獨立的影像,得出有需要辨識的相關區域影像(步驟S313),以利以影像處理技術辨識其中內容(步驟S315)。在一實施例中,為了確保自動辨識出來的資訊是可用的,還可驗證內容,其中概念是依照相關格式與邏輯的規則可驗證辨識的文字或數字(步驟S317),才確認內容(步驟S319),以執行後續應用,例如由一金融機構根據客戶提出的申請金融服務的請求,從其中文件中的財力資訊核發一信用額度。Then, in another embodiment, some steps to facilitate subsequent software procedures can be performed, such as cropping the recognition area into an independent image to obtain an image of the relevant area that needs to be recognized (step S313), so as to facilitate image processing. The technology recognizes the content (step S315). In one embodiment, in order to ensure that the automatically recognized information is available, the content can also be verified. The concept is to verify the recognized text or number according to the rules of related format and logic (step S317), and then the content is confirmed (step S319). ) To perform subsequent applications. For example, a financial institution issues a credit line from the financial information in the file based on a customer’s request for financial services.
在此一提的是,在驗證所辨識得出的內容時,可以套用邏輯判斷結果是否合理(rule base),例如所識別出存摺中的金額應有特定格式與位數,相關證號也應具備特定格式。更者,由於文件(如財力證明文件)可用以證明該客戶的財力,其中特別是具有多欄位的資訊,資訊之間的關係應有一定合理性,其中時間與金額資訊彼此也應具備合理的關聯,這些就形成一種驗證內容的規則。如此可以驗證內容是否符合定義,若不合理,即退件或換成人工處理。It is mentioned here that when verifying the identified content, logic can be applied to determine whether the result is reasonable (rule base), for example, the amount in the identified passbook should have a specific format and number of digits, and the relevant certificate number should also be Have a specific format. Moreover, since documents (such as financial proof documents) can be used to prove the customer’s financial resources, especially information with multiple fields, the relationship between the information should be reasonable, and the time and amount information should also be reasonable. These are the rules for verifying content. In this way, it can be verified whether the content meets the definition, and if it is unreasonable, it will be returned or replaced by manual processing.
以上運用了智能模型能有效率地取得文件中的資訊,包括文字、數字與符號等,然而,經過持續不斷地修正,所述智能模型還可增加準確度,優化模型,以利系統運作。在此可採用的機器學習演算法介紹如下。The above uses the intelligent model to efficiently obtain the information in the document, including text, numbers, and symbols. However, after continuous correction, the intelligent model can also increase the accuracy and optimize the model to facilitate the operation of the system. The machine learning algorithms that can be used here are introduced as follows.
根據上述第一智能模型的實施例,所採用的第一智能模型演算法實現一種即時物件偵測技術,將輸入影像分割為多個區域(網格),對每個區域進行物件定位與分類,並演算出當中物件的邊界框及其對應的機率,也就是先通過辨識每個區域的影像中具有特定物件的類別並計算具有特定物件的機率,以此預測是否有預設的物件。根據揭露書提出的文件內容識別方法的實施例,第一智能模型用以根據當中物件類別與機率得出文件中的各種欄位中各物件的位置,此例顯示為文字或數字的位置,以在文件的影像上框選出判斷為有意義文字或數字涵蓋的第一類區域,而其中每個文字或數字框都有區域邊界。如此,以客戶財力的存摺或扣繳憑單等文件為例,利用將大量存摺或扣繳憑單影像輸入以機器學習演算法訓練得出的第一智能模型,得出各種文件所記載的各欄位資訊,如日期與時間欄位、摘要欄位、提款與存款欄位,以及結餘欄位,使得系統可以得出文件影像中的文字或數字的位置(第一類區域)。According to the above-mentioned first smart model embodiment, the adopted first smart model algorithm implements a real-time object detection technology, which divides the input image into multiple regions (grids), and locates and classifies objects in each region. And calculate the bounding box of the object in it and its corresponding probability, that is, firstly identify the category of the specific object in the image of each area and calculate the probability of having the specific object to predict whether there is a preset object. According to the embodiment of the document content recognition method proposed in the disclosure, the first intelligent model is used to obtain the position of each object in various fields in the document according to the type and probability of the object in the document. In this example, the position is displayed as a text or number. Frame the first type of area judged to be covered by meaningful text or numbers on the image of the document, and each text or number frame has an area boundary. In this way, taking documents such as the customer’s bankbook or withholding voucher as an example, the first intelligent model trained by machine learning algorithms is used to input a large number of passbooks or withholding voucher images to obtain the fields recorded in various documents Information, such as date and time fields, summary fields, withdrawal and deposit fields, and balance fields, allows the system to find the position of the text or number in the document image (the first type of area).
在第二智能模型的實施例中,採用第二智能模型演算法實現的物件辨識技術,為考量了各種形式的文件中的文字可能為不規則的形狀,不容易用傳統影像文字識別的方法取得值得信任的資訊,於是採用了可以取得字元與字元間關聯以識別內容的機器學習法,學習文件中字元,以及預測字元之間的關聯性,以判斷出是否為同一文字或數字,通過反覆大量數據訓練優化模型,得出辨識文字或數字的第二智能模型。此例中,通過第二智能模型演算法可以產出兩張熱點圖,第一張熱點圖為每個字符中心點機率,第二張熱點圖為相連字符之間空間的中心點的機率。透過此兩張熱點圖來取得字詞邊框,判斷出具有文字或數字的第二類區域,而不是直接辨識內容本身,因此可以針對影像中不規則形狀的文字或數字區域進行判斷。In the embodiment of the second smart model, the object recognition technology realized by the second smart model algorithm is used to consider that the text in various forms of documents may be irregular shapes, and it is not easy to obtain by traditional image text recognition methods. Trustworthy information, so a machine learning method that can obtain the association between characters and characters to identify the content is used to learn the characters in the document and predict the association between the characters to determine whether they are the same text or number. Through repeated training of a large amount of data to optimize the model, a second intelligent model for recognizing characters or numbers is obtained. In this example, two heat maps can be generated through the second intelligent model algorithm. The first heat map is the probability of the center point of each character, and the second heat map is the probability of the center point of the space between connected characters. Using these two heat maps to obtain the word borders, determine the second type of area with text or numbers, instead of directly identifying the content itself, so it can be judged for the irregularly shaped text or number areas in the image.
經過以上智能模型得出文件中有意義的區域,以存摺為例,相關欄位如時間(日期)、摘要、金額等,所述有意義的區域記載的內容為可得出用戶財力資訊,例如薪資(如平均月薪資)與存款餘額等資訊的相關內容。之後可以經過文字或數字辨識技術得出實際的內容。After the above intelligent model, the meaningful area in the file is obtained. Take the passbook as an example, the relevant fields such as time (date), summary, amount, etc., the content recorded in the meaningful area is the user's financial information, such as salary ( Such as the average monthly salary) and the relevant content of the deposit balance and other information. Afterwards, the actual content can be obtained through text or digital recognition technology.
圖4顯示執行文件內容識別方法的範例示意圖,此例顯示為客戶可以提出的財力證明文件之一,即存摺內頁,圖中顯示存摺內頁的範例圖式,其中涵蓋多種中英文欄位資訊,經套用上述智能模型後,可以辨識出其中多個辨識區域(401~411)。Figure 4 shows an example schematic diagram of the implementation of the document content identification method. This example shows one of the financial proof documents that the customer can submit, that is, the inner page of the passbook. The figure shows the sample pattern of the inner page of the passbook, which covers a variety of Chinese and English field information After applying the above-mentioned smart model, multiple recognition areas (401-411) can be identified.
根據此圖例,日期(401)與DATE(402)表示一筆交易的時間(403),內容如:「1050129」、「1050202」等;摘要(404)與MEMO(405)顯示此筆交易的動作與相關交易碼,如:「跨行提款」、「跨行轉入」;提款(406)與WITHDRAWAL(407)顯示交易中的提款金額,如:「$20,005.00」;存款(408)與DEPOSIT(409)欄位顯示存款金額,如:「$18,000.00」;最後顯示有結餘(410)以及BALANCE(411),此例顯示幾筆交易後的餘額,如:「$132,747.0」、「$150,747.0」等。According to this legend, the date (401) and DATE (402) indicate the time (403) of a transaction, such as "1050129", "1050202", etc.; the summary (404) and MEMO (405) show the actions and actions of this transaction Related transaction codes, such as: "Inter-bank withdrawal", "Inter-bank transfer in"; Withdrawal (406) and WITHDRAWAL (407) display the withdrawal amount in the transaction, such as: "$20,005.00"; Deposit (408) and DEPOSIT (409) ) Column displays the deposit amount, such as: "$18,000.00"; finally, it displays a balance (410) and BALANCE (411). This example displays the balance after several transactions, such as: "$132,747.0", "$150,747.0", etc.
值得一提的是,如圖示的範例,存摺中雖設計有明確的欄位,但其中記錄往往因為列印定位的問題會有偏移,也可能因為同時有其他資訊在當中顯得一般文字辨識技術(如Optical Character Recognition,OCR)無法準確辨識當中資訊,特別是用於辨識需要更為嚴格辨識技術的財力證明文件。It is worth mentioning that, like the example shown in the figure, although there are clear fields in the passbook, the records in the passbook are often offset due to printing positioning problems, or because there are other information in it that appear to be generally text-recognizable. Technology (such as Optical Character Recognition, OCR) cannot accurately identify the information in it, especially for identifying financial documents that require more rigorous identification technology.
根據此圖例,顯示出原本存摺中沒有的方框,這是揭露書所提出的文件內容識別方法中採用了智能模型,根據上述實施例的描述,例如可應用上述實施例所描述的第一智能模型演算法(並非用於限制所述方法)訓練得出的第一智能模型,以能根據存摺中定義好的多個欄位定位出各欄位的文字或數字,並可以方框框選出所辨識得出的辨識區域,如此例框選出當中文字或數字涵蓋的第一類區域。According to this legend, the boxes that are not in the original passbook are displayed. This is the smart model used in the document content recognition method proposed in the disclosure book. According to the description of the above embodiment, for example, the first smart described in the above embodiment can be applied. The first intelligent model trained by the model algorithm (not used to limit the method) can locate the text or number in each field according to the multiple fields defined in the passbook, and can select the recognized For the obtained recognition area, select the first type of area covered by the text or number in this example.
更者,例如還可應用上述實施例所描述第二智能模型演算法(並非用於限制所述方法)訓練得出的第二智能模型判斷存摺的文字或數字熱點區,接著可以邏輯判斷字元的意思與字元之間的關係,使得智能模型可以從存摺中證明文件的影像中框選出具有文字或數字的第二類區域。Moreover, for example, the second smart model trained by the second smart model algorithm described in the above embodiment (not used to limit the method) can be used to determine the text or digital hotspot area of the passbook, and then the characters can be logically determined The relationship between the meaning of and the characters allows the intelligent model to frame the second type of area with words or numbers from the image of the proof document in the passbook.
針對鄰接不同類的區域之間面積相關性的實施例,可參考圖6A,其中顯示鄰接兩個通過第一、第二智能模型框選的區域,此例顯示被框選的的辨識區域一601與辨識區域二603,最終會以兩個辨識區域的聯集做為辨識區域。圖6B顯示鄰接兩個通過不同智能模型框選的辨識區域三605與辨識區域四607,但這兩個鄰接區域重疊的面積較小。For an embodiment of the area correlation between adjacent areas of different types, refer to FIG. 6A, which shows two adjacent areas selected by the first and second smart models. In this example, the frame-selected
由於不同智能模型的演算與學習過程不同,針對相同資訊來看可能有所不同的學習,如此,在揭露書提出的文件內容識別方法還整合了第一、第二(或更多)智能模型得出的結果,根據鄰接的第一類區域與第二類區域的相關性決定所要的辨識區域,例如第一類區域框出該類別所涵蓋的最大範圍,當第一類區域與第二類區域重疊的面積大(如大於系統提出的一門檻)時,表示為有意義的資訊;反之,若重疊的區域小(如低於系統提出的門檻),表示這個區域並非具有有意義內容,即可拋棄。Since the calculation and learning processes of different smart models are different, different learning may be seen for the same information. Therefore, the document content recognition method proposed in the disclosure book also integrates the first, second (or more) smart models. As a result, the required recognition area is determined according to the correlation between the adjacent first-type area and the second-type area. For example, the first-type area frames the maximum range covered by the category, when the first-type area and the second-type area When the overlapping area is large (such as larger than a threshold proposed by the system), it is expressed as meaningful information; on the contrary, if the overlapping area is small (such as lower than the threshold proposed by the system), it means that this area does not have meaningful content and can be discarded.
更者,經過不同智能模型所框選的鄰接區域,如圖6C,若第二類區域之辨識區域五609與辨識區域六611包含於第一類區域之辨識區域七613之中,則將第二類區域之辨識區域五609與辨識區域六611合併為一辨識區域。Furthermore, after the adjacent areas selected by different smart models, as shown in Fig. 6C, if the
之後,當得出存摺等證明文件中的辨識區域後, 可以對影像進行裁切,實施例如圖5A、5B所示執行文件內容識別方法的範例示意圖。After that, when the identification area in the certificate document such as the passbook is obtained, the image can be cropped, for example, the schematic diagram of the implementation of the document content identification method shown in FIGS. 5A and 5B is implemented.
圖5A示意將存摺中的時間(日期)欄位的資訊裁切出來,圖5B則示意顯示裁切出每次交易餘額的資訊,餘額配合日期,可以讓系統判斷出客戶的財力資訊。Figure 5A shows the cutting out of the information in the time (date) field in the passbook. Figure 5B shows the cutting out information of the balance of each transaction. The balance matches the date, allowing the system to determine the customer's financial information.
根據所述文件內容識別方法實施例,執行文字辨識時,即引入一個以上的辨識方法(模型),可進行雙重確認來排除錯誤,也因為不同機器學習演算法對物件辨識、影像定位的差異,若兩個辨識結果一致,即為最終辨識結果。According to the document content recognition method embodiment, when performing text recognition, more than one recognition method (model) is introduced, and double confirmation can be performed to eliminate errors. Also because of the differences in object recognition and image positioning by different machine learning algorithms, If the two identification results are consistent, it is the final identification result.
綜上所述,根據以上實施例所描述的應用文件內容識別技術的金融服務申請審核系統,當接收到客戶端上傳的文件影像檔後,可以利用智能模型快速辨識出證明文件中多個辨識區域中的文字或數字,經得出財力資訊後,例如計算出存摺結餘、扣繳憑單中的年收入或其他證明文件的財力數據,使得銀行等相關機構能快速處理客戶貸款、信用卡等申請需求。In summary, according to the financial service application review system using the document content recognition technology described in the above embodiments, after receiving the file image file uploaded by the client, the intelligent model can be used to quickly identify multiple identification areas in the certification file After obtaining financial information, such as calculating the balance of the passbook, the annual income in the withholding voucher, or the financial data of other supporting documents, the text or numbers in the Chinese bank can quickly process customer loan, credit card and other application requirements.
以上所公開的內容僅為本新型的優選可行實施例,並非因此侷限本新型的申請專利範圍,所以凡是運用本新型說明書及圖式內容所做的等效技術變化,均包含於本新型的申請專利範圍內。The content disclosed above is only a preferred and feasible embodiment of the present model, and does not limit the scope of the patent application of the present model. Therefore, all equivalent technical changes made by using the description and schematic content of the present model are included in the application of the present model. Within the scope of the patent.
11:伺服系統
13:數據庫
10:網路
101, 103:客戶端裝置
105:自助式金融服務裝置
111:模糊判斷模組
112:財證分類模組
113:影像校正模組
114:物件偵測模組
115:定位修正模組
116:文數字辨識模組
117:驗證修正模組
118:財力計算模組
401~411:辨識區域
601:辨識區域一
603:辨識區域二
605:辨識區域三
607:辨識區域四
609:辨識區域五
611:辨識區域六
613:辨識區域七
步驟S201~S213:文件內容識別流程
步驟S301~S319:金融服務申請審核流程
11: Servo system
13: database
10:
圖1顯示為金融服務申請審核系統架構實施例圖;Figure 1 shows an embodiment diagram of the financial service application review system architecture;
圖2顯示為系統中應用的金融服務申請審核方法的實施例流程圖;Figure 2 shows a flowchart of an embodiment of a financial service application review method applied in the system;
圖3顯示文件內容識別方法的實施例流程圖;Fig. 3 shows a flowchart of an embodiment of a method for recognizing file content;
圖4顯示執行文件內容識別方法的範例示意圖之一;Figure 4 shows one of the exemplary schematic diagrams of executing the method of document content recognition;
圖5A、5B顯示執行文件內容識別方法的範例示意圖之二;以及5A and 5B show the second schematic diagram of an example of executing the method for identifying the content of a document; and
圖6A、6B與6C顯示執行文件內容識別方法中確認辨識區域的範例示意圖。6A, 6B, and 6C show exemplary schematic diagrams of confirming the recognition area in the execution of the document content recognition method.
11:伺服系統 11: Servo system
13:數據庫 13: database
10:網路 10: Internet
101,103:客戶端裝置 101, 103: client device
105:自助式金融服務裝置 105: Self-service financial service device
111:模糊判斷模組 111: fuzzy judgment module
112:財證分類模組 112: Financial Certificate Classification Module
113:影像校正模組 113: Image correction module
114:物件偵測模組 114: Object detection module
115:定位修正模組 115: positioning correction module
116:文數字辨識模組 116: Character and Number Recognition Module
117:驗證修正模組 117: Verify and correct the module
118:財力計算模組 118: Financial calculation module
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