JP2009301411A - Image processing method and image processing device for sampling embossed characters - Google Patents
Image processing method and image processing device for sampling embossed characters Download PDFInfo
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Abstract
Description
本発明は、形状がわからない又は形状データ未取得な曲面に施された凹凸文字を認識する際に好適な凹凸文字抽出のための画像処理方法及び画像処理装置に関する。 The present invention relates to an image processing method and an image processing apparatus for extracting uneven characters suitable for recognizing uneven characters applied to a curved surface whose shape is unknown or whose shape data is not acquired.
工場等における生産管理の一例として、個々の中間製品等に対して刻印により固有の識別番号を施し、それをＣＣＤカメラや２次元変位計(凹凸センサ)等の計測機器を用いて検出し、コンピュータを用いて自動的に文字を認識させることで、製造工程及び在庫を管理する事が行なわれている。
このような凹凸文字を認識する技術として以下の公報に開示された技術がある。
特開２００７−１１６５４号公報（特許文献１）は、カードの表面に形成されているエンボス文字を暗視野照明法によって正確に読み取ることができるエンボス文字読取方法を開示する。
As an example of production management in factories, etc., a unique identification number is given to each intermediate product by imprinting, and it is detected using a measuring device such as a CCD camera or a two-dimensional displacement meter (concave / convex sensor). The manufacturing process and inventory are managed by automatically recognizing characters using.
As a technique for recognizing such uneven characters, there is a technique disclosed in the following publication.
Japanese Unexamined Patent Application Publication No. 2007-11654 (Patent Document 1) discloses an embossed character reading method that can accurately read an embossed character formed on the surface of a card by a dark field illumination method.
このエンボス文字読取方法は、カードの一方辺の両側及び他方辺の両側から前記カードのカード表面を照明する照明光の照明条件を変えて、カード表面を繰り返し撮影するカード表面撮影工程と、異なる照明条件の下で取得された各カード表面撮影画像に基づき、エンボス文字を識別する識別工程とを含む。このエンボス文字読取方法によると、照明光の照射角度を多段階に切り換えて撮影したカード表面撮影画像の中から、エンボス文字輪郭とカード背景のコントラストの高い鮮明な撮影画像を選択して正確に文字認識を行なうことができる。 This embossed character reading method is different from the card surface photographing step of photographing the card surface repeatedly by changing the illumination conditions of the illumination light that illuminates the card surface of the card from both sides of the one side and the other side of the card. And an identification step for identifying embossed characters based on each card surface photographed image obtained under conditions. According to this embossed character reading method, a clear photographed image with high contrast between the embossed character outline and the card background is selected from the card surface photographed images obtained by switching the illumination light irradiation angle in multiple stages. Recognition can be performed.
さらに、特開昭６１−１３１０８３号公報（特許文献２）は、タイヤのサイドウォール部に形成された文字を誤差なく正確に読取ることができるタイヤの文字認識方法を開示する。
この文字認識方法は、タイヤに設けられた刻印をセンサで走査し、その走査方向及び刻印の高さ方向の２つの出力を取出し、高さ方向出力の２つのレベル間の面積を求め、これを走査順に２値化して時系列的に演算装置に記憶させ、次いで得られたデータを予め演算装置のメモリに記憶された各種文字形状の２値化データと比較して読取文字の種別を判定するようにしたことを特徴とする。この文字認識方法によると、タイヤの形状、色、艶に影響されず、また、タイヤに付す文字形状に制限なく、さらには照明装置に特別な工夫がなくても、タイヤ上の文字を正確に信頼性高く読取ることができる。
In this character recognition method, a mark provided on a tire is scanned with a sensor, two outputs in the scanning direction and the height direction of the mark are taken out, an area between two levels of the height direction output is obtained, and this is obtained. The binarization is performed in the scanning order and stored in the arithmetic device in time series, and then the obtained data is compared with the binary data of various character shapes stored in advance in the memory of the arithmetic device to determine the type of the read character. It is characterized by doing so. This character recognition method does not affect the shape, color, or gloss of the tire, and does not limit the character shape that is attached to the tire. It can be read with high reliability.
画像処理を用いた文字認識において、曲面状の対象物に施された凹凸文字を認識するときには、基準となる「対象物の曲面形状」が事前にわかっていることは少ない。また、大まかにはわかっていたとしても、対象毎に若干形状が異なることはよくあることである。そのため、精度の高い文字認識が困難となっていた。
さらには、成型後の製品に施された刻印のように対象物の表面形状に歪みが生じ得る状況下で凹凸文字を認識するときには、凹凸文字の段差量が小さい（凹凸文字の品質が悪い）と影も小さくなり文字を見つけづらくなることがある。
In character recognition using image processing, when a concave / convex character applied to a curved object is recognized, the “curved surface shape of the object” as a reference is rarely known in advance. Moreover, even if it is roughly known, the shape is often slightly different for each object. Therefore, it has been difficult to recognize characters with high accuracy.
Furthermore, when recognizing uneven characters in a situation where the surface shape of the object may be distorted, such as a stamp applied to the product after molding, the step amount of the uneven characters is small (the quality of the uneven characters is poor). And the shadow will be smaller and it may be difficult to find the characters.
これらの問題に対して、特許文献１の技術を適用しようとしても、この技術は、クレジットカードなどの平板上のエンボス文字の抽出に限定されたものを対象としているため、これ以外の、特に対象物表面の基準的な形状が事前にわからない場合には文字の位置を特定することは困難である。
特許文献２に開示された技術では、対象物をタイヤのサイドウォールの凸文字に限定しているので、これ以外、特に対象物表面の基準的な形状が事前にわからない場合の文字抽出を行なうことは困難である。加えて、文字認識には「２値化」の技術を用いているが、２値化技術はその閾値の取り方により得られるデータが大きく異なり、文字検出精度を一定に保つことが困難であったりする。
Even if it is going to apply the technique of patent document 1 with respect to these problems, since this technique is limited to the extraction of the embossed character on flat plates, such as a credit card, it is a target especially other than this. When the standard shape of the object surface is not known in advance, it is difficult to specify the position of the character.
In the technique disclosed in Patent Document 2, since the object is limited to convex characters on the sidewall of the tire, character extraction is performed when the reference shape of the object surface is not known in advance. It is difficult. In addition, the “binarization” technology is used for character recognition, but the data obtained by the binarization technology differs greatly depending on the threshold value, and it is difficult to keep the character detection accuracy constant. Or
さらに、いずれの特許文献においても、凹凸の段差量（高さや深さ）の計測については言及がない。
そこで、本発明は、上記問題点を鑑み、形状が事前に判明していない曲面に施された凹凸文字を精度高く読取ることができる凹凸文字抽出のための画像処理方法及び画像処理装置を提供することを目的とする。
Furthermore, in any of the patent documents, there is no mention of measurement of the uneven step height (height or depth).
Therefore, in view of the above problems, the present invention provides an image processing method and image processing apparatus for extracting uneven characters that can accurately read uneven characters applied to a curved surface whose shape is not known in advance. For the purpose.
上述の目的を達成するため、本発明においては以下の技術的手段を講じた。
本発明に係る凹凸文字抽出のための画像処理方法は、表面形状データが未取得の任意の曲面に施された凹凸文字の画像を抽出するものであって、前記凹凸文字を含んだ曲面の３次元データを取得する３次元計測ステップと、得られた前記３次元データに基づき、水平面内の各点における高さデータの微分値を算出する微分ステップと、算出された前記微分値に基づいて、前記各点の高さデータに対する重みを算出する重み付けステップと、前記重みと前記各点の高さデータとを用いて、前記凹凸文字が刻印される前の曲面の形状を近似した近似曲面の３次元データを算出する近似曲面算出ステップと、前記凹凸文字を含んだ曲面の３次元データと、前記近似曲面の３次元データとから、前記凹凸文字の３次元データを抽出する凹凸文字抽出ステップと、を有することを特徴とする。
In order to achieve the above-described object, the present invention takes the following technical means.
An image processing method for extracting uneven characters according to the present invention extracts an image of uneven characters applied to an arbitrary curved surface for which surface shape data has not been acquired. Based on the calculated three-dimensional measurement step, the differential step of calculating the differential value of the height data at each point in the horizontal plane based on the obtained three-dimensional data, and the calculated differential value, 3, an approximate curved surface that approximates the shape of the curved surface before the concave and convex characters are imprinted by using a weighting step for calculating a weight for the height data of each point, and the weight and the height data for each point. Approximate curved surface calculating step for calculating dimensional data, three-dimensional data of the curved surface including the concavo-convex characters, and three-dimensional data of the concavo-convex characters to extract the three-dimensional data of the concavo-convex characters. Characterized in that it has Tsu and up, the.
これにより、凹凸文字が付与された曲面形状が予め正確にわかっていなくとも、凹凸文字の３次元データ（位置、段差量）を確実に認識でき、正確な文字認識も可能となる。
好ましくは、前記凹凸文字抽出ステップでは、前記凹凸文字を含んだ曲面の高さデータから前記近似曲面の高さデータを減算することで、凹凸文字の段差量を算出するとよい。
前記近似曲面算出ステップでは、凹凸文字に対応する部分の重みを零又は低くした条件のもとで重み付き最小二乗法を用いて、前記近似曲面の３次元データを算出するとよい。
凹凸文字が施される前の曲面データ（近似曲面データ）を正確に算出するに際しては、まず、高さデータを微分（２次微分が好ましい）した値に基づいて、各点のＺ方向座標値の重みを算出する。Ｚ方向座標値の微分値は、急峻な変化（文字を形成する凹部や凸部）で大きな値になるため、この特性を用いて文字近傍の重みを小さく算出する。この重みを用いることで、文字のある部分を除外して（低い重みとして）、曲面の形状を近似することができ、高精度に近似曲面データを算出することができる。すなわち、表面形状データが未取得の任意の曲面に施された凹凸文字が存在しても、近似曲面を正確に算出することができる。
This makes it possible to reliably recognize the three-dimensional data (position and step amount) of the uneven character and accurately recognize the character even if the curved surface shape to which the uneven character is given is not accurately known in advance.
Preferably, in the uneven character extraction step, the step amount of the uneven character may be calculated by subtracting the height data of the approximate curved surface from the height data of the curved surface including the uneven character.
In the approximate curved surface calculating step, the three-dimensional data of the approximate curved surface may be calculated using a weighted least square method under the condition that the weight of the portion corresponding to the uneven character is zero or low.
When accurately calculating the curved surface data (approximate curved surface data) before the irregular characters are applied, first, based on the value obtained by differentiating the height data (preferably the second derivative), the Z-direction coordinate value of each point The weight of is calculated. Since the differential value of the Z direction coordinate value becomes a large value due to a steep change (a concave portion or a convex portion forming a character), the weight in the vicinity of the character is calculated to be small using this characteristic. By using this weight, it is possible to approximate the shape of the curved surface by excluding a certain part of the character (as a low weight), and it is possible to calculate the approximate curved surface data with high accuracy. That is, the approximate curved surface can be accurately calculated even if there are uneven characters applied to any curved surface for which surface shape data has not been acquired.
好ましくは、凹凸文字抽出ステップは、前記重みを用いて前記凹凸文字の段差量の加重平均値を算出し、前記加重平均値が予め定められた閾値以上であるときには、凹凸文字の品質が良いと判定するステップを含むとよい。
本発明に係る凹凸文字抽出のための画像処理装置は、表面形状データが未取得の任意の曲面に施された凹凸文字の画像を抽出するものであって、前記凹凸文字を含んだ曲面の３次元データを取得する３次元計測手段と、得られた前記３次元データに基づき、水平面内の各点における高さデータの微分値を算出する微分手段と、算出された前記微分値に基づいて、前記各点の高さデータに対する重みを算出する重み付け手段と、前記重みと前記各点の高さデータとを用いて、前記凹凸文字が刻印される前の曲面の形状を近似した近似曲面の３次元データを算出する近似曲面算出手段と、前記凹凸文字を含んだ曲面の３次元データと、前記近似曲面の３次元データとから、前記凹凸文字の３次元データを抽出する凹凸文字抽出手段と、を有することを特徴とする。
Preferably, the uneven character extraction step calculates a weighted average value of the step amount of the uneven character using the weight, and the quality of the uneven character is good when the weighted average value is equal to or greater than a predetermined threshold. The step of determining may be included.
An image processing apparatus for extracting uneven characters according to the present invention extracts an image of uneven characters applied to an arbitrary curved surface for which surface shape data has not been acquired. Based on the calculated three-dimensional measurement means, the differential means for calculating the differential value of the height data at each point in the horizontal plane based on the obtained three-dimensional data, and the calculated differential value, 3, an approximate curved surface that approximates the shape of the curved surface before the concave and convex characters are engraved by using weighting means for calculating weights for the height data of the points, and using the weights and the height data of the points. Approximate curved surface calculating means for calculating dimensional data, three-dimensional data of a curved surface including the concavo-convex characters, and three-dimensional data of the concavo-convex characters from the three-dimensional data of the approximate curved surface; Have The features.
本発明によると、形状が事前に判明していない曲面に施された凹凸文字を精度高く読取ることができる。 According to the present invention, it is possible to accurately read uneven characters applied to a curved surface whose shape is not known in advance.
以下、本発明の実施形態を、図を基に説明する。
なお、以下の説明では、同一の部品には同一の符号を付してある。それらの名称及び機能も同じである。したがって、それらについての詳細な説明は繰返さない。
図１を参照して、本実施形態に係る凹凸文字抽出のための画像処理（以降、画像処理装置）の全体システム構成について説明する。
この画像処理装置は、光切断法を適用した光切断法センサ１を有している。光切断法センサ１は、対象物３にライン状の光切断線（ライン状のレーザー光）を投射する投射部１１と、対象物３の表面から反射してきた光切断線を撮像する撮像部１２とを有している。
Hereinafter, embodiments of the present invention will be described with reference to the drawings.
In the following description, the same parts are denoted by the same reference numerals. Their names and functions are also the same. Therefore, detailed description thereof will not be repeated.
With reference to FIG. 1, an overall system configuration of image processing (hereinafter referred to as an image processing apparatus) for extracting uneven characters according to the present embodiment will be described.
This image processing apparatus has a light cutting method sensor 1 to which a light cutting method is applied. The light cutting method sensor 1 includes a projection unit 11 that projects a line-shaped light cutting line (line-shaped laser light) onto the object 3, and an imaging unit 12 that images the light cutting line reflected from the surface of the object 3. And have.
さらに、画像処理装置は、光切断法センサ１を対象物３に沿って走査するための走査用レール２を有している。これは、光切断法センサ１による１回の計測では、光切断線が当たった部分しか凹凸情報を取得できないため、当該走査用レール２を用いて光切断法センサ１を対象物３に倣う方向に走査させることにより、対象物３全体の表面形状を計測するためである。
また、画像処理装置は、光切断法センサ１に接続されて光切断法センサ１からの信号を受信して処理するパーソナルコンピュータ（以下パソコン）４を有する。なお、パソコン４による凹凸文字の読取結果はホストコンピュータ５に送信されるようになっている。
Further, the image processing apparatus has a scanning rail 2 for scanning the light cutting method sensor 1 along the object 3. This is because in one measurement by the light-cutting method sensor 1, the unevenness information can be acquired only at the portion where the light-cutting line hits, and thus the direction in which the light-cutting method sensor 1 is imitated with the object 3 using the scanning rail 2. This is because the surface shape of the entire target object 3 is measured by scanning.
Further, the image processing apparatus includes a personal computer (hereinafter referred to as a personal computer) 4 that is connected to the light cutting method sensor 1 and receives and processes a signal from the light cutting method sensor 1. The result of reading uneven characters by the personal computer 4 is transmitted to the host computer 5.
対象物３は湾曲した金属板や円筒物表面であって、画像処理装置は、対象物３上に施された凹凸文字を読取る。
詳しくは、光切断法センサ１を走査用レール２により対象物３に倣う方向に平行移動させていくことで、投射部１１から対象物３の表面に光切断線を照射し、それと同時に、対象物３から反射してくる光切断線の画像データを撮像部１２にて取得する。
取得された画像データは、パソコン４内に取り込まれ、パソコン４内で三角測量法の原理に基づいた処理を施されることで、対象物３の３次元形状データ（凹凸情報を含む）が得られる。その後、対象物３上に刻印された凹凸文字を抽出して、読取結果をホストコンピュータ５に送信する。
The object 3 is a curved metal plate or cylindrical surface, and the image processing apparatus reads uneven characters applied on the object 3.
Specifically, the light cutting method sensor 1 is moved in parallel in a direction following the object 3 by the scanning rail 2, thereby irradiating the surface of the object 3 with the light cutting line from the projection unit 11. The image data of the light cutting line reflected from the object 3 is acquired by the imaging unit 12.
The acquired image data is taken into the personal computer 4 and processed based on the principle of the triangulation method in the personal computer 4 to obtain three-dimensional shape data (including unevenness information) of the object 3. It is done. Thereafter, the uneven characters stamped on the object 3 are extracted, and the reading result is transmitted to the host computer 5.
図２を参照して、パソコン４で実行される画像処理の概要について説明する。
本画像処理は、まず、対象物３の表面曲面データを取得する３次元計測ステップを実施し（Ｓ０）、得られた３次元データに基づき、水平面内の各点における高さデータの２次微分値を算出する微分ステップを行う（Ｓ１）。
その後、算出した２次微分値に基づいて各点の高さデータに対する「重み」を計算する重み付けステップを実行し（Ｓ２）、水平面データ及び高さデータ及び重み値に基づいて、対象物の表面曲面形状をＮ次の重み付き最小二乗法で近似し、近似曲面を算出する近似曲面算出ステップを行う（Ｓ３）。なお、近似曲面とは、凹凸文字が刻印される以前における対象物３の表面曲面を計算で求めたものである。
With reference to FIG. 2, an outline of image processing executed by the personal computer 4 will be described.
In this image processing, first, a three-dimensional measurement step for acquiring the surface curved surface data of the object 3 is performed (S0), and based on the obtained three-dimensional data, the second derivative of the height data at each point in the horizontal plane. A differentiation step for calculating a value is performed (S1).
Thereafter, a weighting step of calculating a “weight” for the height data of each point based on the calculated secondary differential value is executed (S2), and based on the horizontal plane data, the height data, and the weight value, the surface of the object An approximate curved surface calculation step of approximating the curved surface shape by an Nth order weighted least square method and calculating an approximate curved surface is performed (S3). The approximate curved surface is obtained by calculating the surface curved surface of the object 3 before the uneven characters are engraved.
次に、Ｘ−Ｙ平面上の各点において、近似曲面と実際の表面形状データとの差分から、凹凸文字の段差の大きさを計算する凹凸文字抽出ステップを実施し（Ｓ４）、段差の大きさを輝度値に変換することにより、凹凸文字画像を生成する（Ｓ５）
生成した文字画像に対して画像処理や文字認識処理を実行することで、凹凸文字を読取り、その結果を出力する（Ｓ６）。
以下、これらに各ステップについて、図２，図３を用いてさらに詳しく説明する。
まず、図２のＳ０にて、光切断法センサ１及びパソコン４内のプログラム（公知の光切断法に基づく）を用い、対象物３の表面形状を計測する。
Next, at each point on the XY plane, an uneven character extraction step is performed for calculating the size of the uneven character step from the difference between the approximate curved surface and the actual surface shape data (S4). An uneven character image is generated by converting the height into a luminance value (S5).
By executing image processing and character recognition processing on the generated character image, the uneven character is read and the result is output (S6).
Hereinafter, each step will be described in more detail with reference to FIGS.
First, at S0 in FIG. 2, the surface shape of the object 3 is measured using the light cutting method sensor 1 and a program in the personal computer 4 (based on a known light cutting method).
なお、計測によって得られた対象物３の表面形状データは、凹凸文字の段差（高さ）方向をＺ軸と定義し、このＺ軸に直行する方向をＸ軸及びＹ軸と定義する。以下の説明においては、Ｘ−Ｙ平面上の各点におけるＺ軸座標値をＺ（ｘ，ｙ）と記載する。このＳ０での処理により、図３（Ａ）に示すような曲率が一定ではない曲面の３次元データが収集される。
図３（Ｂ）は、対象物３の表面に施された凹凸文字のサンプルデータであり、Ｘ−Ｙ平面上の各点におけるＺ軸座標を輝度値に変換した画像である。色が白いほど、Ｚ軸座標が大きい値になっている。
In the surface shape data of the object 3 obtained by measurement, the step (height) direction of the concavo-convex characters is defined as the Z axis, and the directions perpendicular to the Z axis are defined as the X axis and the Y axis. In the following description, the Z-axis coordinate value at each point on the XY plane is described as Z (x, y). By the processing at S0, the three-dimensional data of the curved surface having a constant curvature as shown in FIG. 3A is collected.
FIG. 3B is sample data of uneven characters applied to the surface of the object 3, and is an image obtained by converting the Z-axis coordinates at each point on the XY plane into luminance values. The whiter the color, the larger the Z-axis coordinate.
Ｓ１にて、Ｘ−Ｙ平面上の各点におけるＺ軸座標値の２次微分値Ｚ’’（ｘ，ｙ）を計算する。２次微分の計算方法は、式（１）〜式（３）に基づく。なお、本実施形態は１次微分や３次以上の高次の微分の適用を積極的に排除するものではなく、対象物によっては１次微分や３次微分の適用も可能である。 In S1, a secondary differential value Z ″ (x, y) of the Z-axis coordinate value at each point on the XY plane is calculated. The calculation method of the second derivative is based on the formulas (1) to (3). In addition, this embodiment does not positively exclude the application of the first-order differentiation or the third-order or higher-order differentiation, and the first-order differentiation or the third-order differentiation can be applied depending on the object.
この２次微分値Ｚ’’（ｘ，ｙ）を用いることで、凹凸文字の位置の概略特定は可能であるが、この２次微分値Ｚ’’（ｘ，ｙ）だけでは、凹凸文字の段差量（高さや深さ）はわからないので、読取と同時に凹凸文字の品質検査を行なうために、以下の処理が必要になる。なお、文字の段差量が大きく文字がハッキリとしていて、読み取りや文字認識が行いやすいものを「文字の品質」が高いということとする。
次に、Ｓ１にて算出した２次微分値Ｚ’’（ｘ，ｙ）に基づいて、Ｓ２で各データ点Ｚ（ｘ，ｙ）の重みＷ（ｘ，ｙ）を計算する。
By using this secondary differential value Z ″ (x, y), it is possible to roughly specify the position of the concave / convex character, but only with this secondary differential value Z ″ (x, y), Since the amount of step (height and depth) is not known, the following processing is required in order to check the quality of the uneven characters simultaneously with reading. It is assumed that “character quality” is high when a character has a large level difference and is easily read and recognized.
Next, based on the secondary differential value Z ″ (x, y) calculated in S1, the weight W (x, y) of each data point Z (x, y) is calculated in S2.
図３（Ａ）に対して重みの計算を行なった結果を図３（Ｃ）に示す。図３（Ｃ）においては、色が白いほど重みが大きく（Ｗ（ｘ，ｙ）≒１）、色が黒いほど重みが小さい又は零である（Ｗ（ｘ，ｙ）≒０）ことを表す。重みの計算は、以下のように行われる。
まず、式（４）に基づいて、各点（各ピクセル）の暫定的な重みＳ（ｘ，ｙ）を計算する。
FIG. 3C shows the result of calculating the weight for FIG. 3A. In FIG. 3C, the whiter the color, the greater the weight (W (x, y) ≈1), and the darker the color, the smaller the weight or zero (W (x, y) ≈0). . The weight is calculated as follows.
First, a temporary weight S (x, y) of each point (each pixel) is calculated based on the equation (4).
ここで、σは全てのｘ，ｙにおける２次微分値Ｚ’’（ｘ，ｙ）の分散値であり、式（４）で表される重みＳ（ｘ，ｙ）は、正規化された正規分布で表現されるものとなっている。この重みＳ（ｘ，ｙ）は、凹凸文字のエッジ部（輪郭部）のみに値を持つものとなるため、そのままでは、凹凸文字部分に対する重みとはならない。そこで、計算したＳ（ｘ，ｙ）を用いて、式（５）から各データ点Ｚ（ｘ，ｙ）の重みＷ（ｘ，ｙ）を計算する。 Here, σ is a variance value of secondary differential values Z ″ (x, y) in all x and y, and the weight S (x, y) expressed by the equation (4) is normalized. It is represented by a normal distribution. Since the weight S (x, y) has a value only at the edge portion (outline portion) of the uneven character, it does not become a weight for the uneven character portion as it is. Therefore, using the calculated S (x, y), the weight W (x, y) of each data point Z (x, y) is calculated from the equation (5).
なお、ａ、ｂは任意のパラメータであって、凹凸文字幅の１／２の大きさ程度の大きさを有する。そのため、式（５）で得られた重みは、凹凸文字の文字線中側や外側においても重みを持つようになり、図３（Ｃ）の結果の如くなる。
次に、Ｓ３では、以上求めた重みＷ（ｘ，ｙ）を用いた最小二乗法により、凹凸文字が刻印される前の曲面の形状を近似した近似曲面の３次元形状を算出する。
詳しくは、図４（Ｂ）に示すように、あるｘ座標（図４（Ａ）のＣ−Ｃ断面）でのｙ−ｚ断面を考える。このｙ−ｚ座標には、曲面の表面形状の断面プロファイルが表れるものとなる。この断面プロファイルに対して、Ｚ軸座標値Ｚ（ｘ，ｙ）と重みＷ（ｘ，ｙ）とを用いた重み付き最小二乗法を適用し、近似曲面の断面プロファイルを算出する。
In addition, a and b are arbitrary parameters, and have a size of about a half of the width of the concavo-convex character. For this reason, the weight obtained by the equation (5) has a weight on the inside and outside of the character line of the uneven character, as shown in the result of FIG.
Next, in S3, the three-dimensional shape of the approximate curved surface that approximates the shape of the curved surface before the concavo-convex characters are engraved is calculated by the least square method using the weight W (x, y) obtained above.
Specifically, as shown in FIG. 4B, consider a yz cross section at a certain x coordinate (CC cross section in FIG. 4A). The yz coordinates represent a cross-sectional profile of the curved surface shape. A weighted least square method using the Z-axis coordinate value Z (x, y) and the weight W (x, y) is applied to the cross-sectional profile to calculate a cross-sectional profile of the approximate curved surface.
この近似法であると、凹凸文字部分及びその近傍は、重みＷ（ｘ，ｙ）≒０であって、最小二乗法の計算へは反映されない状況となる。つまり、凹凸文字部分にマスクをかけた上で、最小二乗法による近似曲面（曲線）の算出を行っていることに相当する。
近似曲面の断面プロファイルをＲ（ｘ，ｙ）とすると、この表面形状Ｒ（ｘ，ｙ）の３次元分布を示したものが図３（Ｄ）である。
また、図４（Ｂ）には、重み付けしない最小二乗法により得られた、近似曲面の断面プロファイルを併記する。図４（Ｂ）の凹部分が凹凸文字部分であるが、重み付けしない最小二乗法で得られた表面形状は、この凹部の形状が加味されるため、対象物３の実際の曲面との乖離が大きいものとなっている。一方で、本方法では、重み付けステップＳ２における重み付け処理により、凹凸文字部の可能性が大きい部位を事前に除去することにより、対象物３の表面曲面の断面プロファイルひいては３次元データを正確に推定できる。
In this approximation method, the uneven character portion and the vicinity thereof have a weight W (x, y) ≈0 and are not reflected in the calculation of the least square method. In other words, this corresponds to calculating an approximate curved surface (curve) by the least square method after masking the uneven character portion.
FIG. 3D shows a three-dimensional distribution of the surface shape R (x, y), where R (x, y) is the cross-sectional profile of the approximate curved surface.
FIG. 4B also shows the cross-sectional profile of the approximate curved surface obtained by the least-square method without weighting. Although the concave portion in FIG. 4B is a concave-convex character portion, the surface shape obtained by the least-square method without weighting takes into account the shape of the concave portion, so that the deviation from the actual curved surface of the object 3 is It has become big. On the other hand, in the present method, the weight profile in the weighting step S2 can accurately estimate the cross-sectional profile of the surface curved surface of the target object 3 and thus the three-dimensional data by removing in advance the portion having a high possibility of the uneven character portion. .
次に、Ｓ４にて、実際の曲面の３次元データＺ（ｘ，ｙ）から近似曲面の３次元データを減じて得られる値Ｄ（ｘ，ｙ）を算出する。この差分値Ｄ（ｘ，ｙ）が、凹凸文字の段差量となる。この演算は、図３（Ｂ）から図３（Ｄ）を減算する部分に相当する。
なお、Ｓ２にて算出した重みＷ（ｘ，ｙ）の逆数１／Ｗ（ｘ，ｙ）を用いつつ、式（６）に基づいて差分値Ｄ（ｘ，ｙ）の加重平均を求めることで、文字の可能性が大きい部分についての段差量（深さや高さ）の平均値μDを求めることができる。
Next, in S4, a value D (x, y) obtained by subtracting the three-dimensional data of the approximate curved surface from the three-dimensional data Z (x, y) of the actual curved surface is calculated. This difference value D (x, y) is the step amount of the uneven character. This calculation corresponds to a portion where FIG. 3D is subtracted from FIG.
In addition, by using the reciprocal 1 / W (x, y) of the weight W (x, y) calculated in S2, the weighted average of the difference value D (x, y) is obtained based on Expression (6). The average value μ D of the step amount (depth and height) can be obtained for a portion having a high possibility of a character.
この値が大きいほど、凹凸文字の段差量が大きくクッキリと刻印がなされていて、文字品質が良いということができる。ゆえに、この値が一定値以上か否かを判定することにより、文字列の文字品質のチェックを行なうことができる。
Ｓ５にて、差分値Ｄ（ｘ，ｙ）を、０〜２５５の値に換え輝度値Ｐ（ｘ，ｙ）に変換する。具体的には、全てのｘ，ｙにおける最小値及び最大値を、それぞれＤmin及びＤmaxとして、式（７）に基づいて、輝度値Ｐ（ｘ，ｙ）を算出する。
It can be said that the larger the value, the larger the level difference of the uneven characters, and the more clearly marked, the better the character quality. Therefore, it is possible to check the character quality of the character string by determining whether or not this value is a certain value or more.
In S5, the difference value D (x, y) is converted to a luminance value P (x, y) by replacing it with a value between 0 and 255. Specifically, the luminance value P (x, y) is calculated based on Expression (7), with the minimum value and the maximum value in all x and y as Dmin and Dmax, respectively.
抽出された凹凸文字データを、式（７）に基づいて輝度画像に変換した結果を図３（Ｅ）に示す。
Ｓ６にて、生成した文字画像（図３（Ｅ））に対して文字認識を実行して、その結果をホストコンピュータ５に送信するようにしている。
なお、比較のため、近似曲面を求めず、図３（Ｂ）に対し単純に２値化処理を行った結果を図３（Ｆ）に示す。これらから、本件の図３（Ｅ）が従来の図３（Ｆ）に比較して、十分にきれいな（誤認識の可能性の極めて低い）文字画像を生成できたことが明らかである。
FIG. 3E shows the result of converting the extracted uneven character data into a luminance image based on Expression (7).
In S6, character recognition is performed on the generated character image (FIG. 3E), and the result is transmitted to the host computer 5.
For comparison, FIG. 3F shows a result of simply performing binarization processing on FIG. 3B without obtaining an approximate curved surface. From these, it is clear that FIG. 3E of the present case was able to generate a sufficiently clean character image (very unlikely to be erroneously recognized) as compared to the conventional FIG. 3F.
以上のようにして、本実施形態に係る画像処理装置によると、曲率が一定ではない(事前に対象物の形状がわかっていない)曲面に施された凹凸文字に対しても、文字以外の背景（文字認識にとってのノイズ）を全て消去して、文字のみが強調された文字画像を生成することができ、正確な文字認識を可能にする。
さらに、読取と同時に凹凸文字の段差量をチェックすることができ、読取の信頼性を高める判断とすることが可能になる。
今回開示された実施形態はすべての点で例示であって制限的なものではないと考えられるべきである。本発明の範囲は上記した説明ではなくて特許請求の範囲によって示され、特許請求の範囲と均等の意味及び範囲内でのすべての変更が含まれることが意図される。
As described above, according to the image processing apparatus according to the present embodiment, the background other than the character is also applied to the uneven character applied to the curved surface whose curvature is not constant (the shape of the object is not known in advance). It is possible to delete all (noise for character recognition) and generate a character image in which only the characters are emphasized, thereby enabling accurate character recognition.
Furthermore, it is possible to check the level difference of the concavo-convex characters at the same time as reading, and it is possible to make a determination that increases the reading reliability.
It should be thought that embodiment disclosed this time is an illustration and restrictive at no points. The scope of the present invention is defined by the terms of the claims, rather than the description above, and is intended to include any modifications within the scope and meaning equivalent to the terms of the claims.
１ 光切断法センサ
２ 走査用レール
３ 対象物
４ パソコン
５ ホストコンピュータ
１１ 投射部
１２ 撮像部
DESCRIPTION OF SYMBOLS 1 Light cutting method sensor 2 Scanning rail 3 Object 4 Personal computer 5 Host computer 11 Projection part 12 Imaging part
Claims (5)
前記凹凸文字を含んだ曲面の３次元データを取得する３次元計測ステップと、
得られた前記３次元データに基づき、水平面内の各点における高さデータの微分値を算出する微分ステップと、
算出された前記微分値に基づいて、前記各点の高さデータに対する重みを算出する重み付けステップと、
前記重みと前記各点の高さデータとを用いて、前記凹凸文字が刻印される前の曲面の形状を近似した近似曲面の３次元データを算出する近似曲面算出ステップと、
前記凹凸文字を含んだ曲面の３次元データと、前記近似曲面の３次元データとから、前記凹凸文字の３次元データを抽出する凹凸文字抽出ステップと、
を有することを特徴とする凹凸文字抽出のための画像処理方法。 An image processing method for extracting an image of uneven characters applied to an arbitrary curved surface for which surface shape data has not been acquired,
A three-dimensional measurement step of acquiring three-dimensional data of a curved surface including the uneven characters;
A differentiation step of calculating a differential value of the height data at each point in the horizontal plane based on the obtained three-dimensional data;
A weighting step for calculating a weight for the height data of each point based on the calculated differential value;
An approximate curved surface calculation step of calculating three-dimensional data of an approximate curved surface that approximates the shape of the curved surface before the uneven character is imprinted using the weight and the height data of each point;
An uneven character extraction step of extracting the three-dimensional data of the uneven character from the three-dimensional data of the curved surface including the uneven character and the three-dimensional data of the approximate curved surface;
An image processing method for extracting uneven characters, characterized by comprising:
前記凹凸文字を含んだ曲面の３次元データを取得する３次元計測手段と、
得られた前記３次元データに基づき、水平面内の各点における高さデータの微分値を算出する微分手段と、
算出された前記微分値に基づいて、前記各点の高さデータに対する重みを算出する重み付け手段と、
前記重みと前記各点の高さデータとを用いて、前記凹凸文字が刻印される前の曲面の形状を近似した近似曲面の３次元データを算出する近似曲面算出手段と、
前記凹凸文字を含んだ曲面の３次元データと、前記近似曲面の３次元データとから、前記凹凸文字の３次元データを抽出する凹凸文字抽出手段と、
を有することを特徴とする凹凸文字抽出のための画像処理装置。 An image processing apparatus that extracts an image of uneven characters applied to an arbitrary curved surface for which surface shape data has not been acquired,
Three-dimensional measuring means for acquiring three-dimensional data of a curved surface including the uneven characters;
Differentiating means for calculating a differential value of the height data at each point in the horizontal plane based on the obtained three-dimensional data;
Weighting means for calculating a weight for the height data of each point based on the calculated differential value;
Using the weight and the height data of each point, approximate curved surface calculation means for calculating three-dimensional data of an approximate curved surface that approximates the shape of the curved surface before the uneven character is imprinted;
An uneven character extracting means for extracting the three-dimensional data of the uneven character from the three-dimensional data of the curved surface including the uneven character and the three-dimensional data of the approximate curved surface;
An image processing apparatus for extracting uneven characters, comprising:
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2011169742A (en) * | 2010-02-18 | 2011-09-01 | Hitachi Ltd | Surface measuring method and device |
| JP2011191131A (en) * | 2010-03-12 | 2011-09-29 | Chubu Electric Power Co Inc | Photogrammetry device and photogrammetry method |
| JP2016146018A (en) * | 2015-02-06 | 2016-08-12 | Ｊｆｅスチール株式会社 | Punch character reader |
| CN111291761A (en) * | 2020-02-17 | 2020-06-16 | 北京百度网讯科技有限公司 | Method and device for recognizing characters |
| CN113657375A (en) * | 2021-07-07 | 2021-11-16 | 西安理工大学 | Bottled object character detection method based on 3D point cloud |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH05303662A (en) * | 1992-04-27 | 1993-11-16 | Aisin Seiki Co Ltd | Stereo character reading method using light-section method |
| JPH08241375A (en) * | 1995-03-02 | 1996-09-17 | Sharp Corp | Binarization processing system for image |
| JPH10222608A (en) * | 1997-02-06 | 1998-08-21 | Toyota Motor Corp | Method and device for inspecting character |
| JP2001091216A (en) * | 1999-09-17 | 2001-04-06 | Anritsu Corp | Device and method for detecting projecting and recessed parts |
| JP2003343243A (en) * | 2002-05-27 | 2003-12-03 | Nissan Motor Co Ltd | Exhaust purifying device of internal combustion engine |
-
2008
- 2008-06-16 JP JP2008156773A patent/JP5036637B2/en not_active Expired - Fee Related
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH05303662A (en) * | 1992-04-27 | 1993-11-16 | Aisin Seiki Co Ltd | Stereo character reading method using light-section method |
| JPH08241375A (en) * | 1995-03-02 | 1996-09-17 | Sharp Corp | Binarization processing system for image |
| JPH10222608A (en) * | 1997-02-06 | 1998-08-21 | Toyota Motor Corp | Method and device for inspecting character |
| JP2001091216A (en) * | 1999-09-17 | 2001-04-06 | Anritsu Corp | Device and method for detecting projecting and recessed parts |
| JP2003343243A (en) * | 2002-05-27 | 2003-12-03 | Nissan Motor Co Ltd | Exhaust purifying device of internal combustion engine |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2011169742A (en) * | 2010-02-18 | 2011-09-01 | Hitachi Ltd | Surface measuring method and device |
| JP2011191131A (en) * | 2010-03-12 | 2011-09-29 | Chubu Electric Power Co Inc | Photogrammetry device and photogrammetry method |
| JP2016146018A (en) * | 2015-02-06 | 2016-08-12 | Ｊｆｅスチール株式会社 | Punch character reader |
| CN111291761A (en) * | 2020-02-17 | 2020-06-16 | 北京百度网讯科技有限公司 | Method and device for recognizing characters |
| CN111291761B (en) * | 2020-02-17 | 2023-08-04 | 北京百度网讯科技有限公司 | Method and device for recognizing text |
| CN113657375A (en) * | 2021-07-07 | 2021-11-16 | 西安理工大学 | Bottled object character detection method based on 3D point cloud |
| CN113657375B (en) * | 2021-07-07 | 2024-04-19 | 西安理工大学 | Bottled object text detection method based on 3D point cloud |
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| Publication number | Publication date |
|---|---|
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