JP4201013B2 - Word recognition device - Google Patents

Description

 The present invention relates to a word recognition device that recognizes handwritten character strings.

 In recent years, there has been an increasing demand for a character recognition device OCR (Optical Character Reader) as a handwritten character input device. Among character recognition devices, in addition to individual character recognition devices, word recognition devices that focus on character strings have appeared in order to improve character recognition.

  FIG. 7A shows a configuration diagram of a conventional individual character recognition device 31.

  The individual character recognition device 31 includes an image input unit 2, a normalization unit 3, a feature extraction unit 4, a feature matching unit 5, a recognition result output unit 6, and an individual character dictionary 7.

  The image input unit 2 acquires an input image of handwritten characters using a scanner or the like. The normalization unit 3 aligns the size and position of the characters to be constant. The feature extraction unit 4 extracts feature amounts from the normalized input image. The feature collation unit 5 collates the character feature amount of the individual character dictionary 7 with the feature amount extracted by the feature extraction unit 4. The recognition result output unit 6 outputs the result collated by the feature collation unit 5 to a display or the like. The individual character dictionary 7 is composed of characters and their feature values.

  The operation will be described with reference to the address example in FIG.

  The character string “Tokyo” input from the image input unit 2 is adjusted to a predetermined size by the normalization unit 3, and then the feature amount of the character is extracted by the feature extraction unit 4. The feature value of “East”, the feature value of “Kyo”, and the feature value of “Miyako” of the extraction result are passed to the feature matching unit 5.

  The feature matching unit 5 compares all the characters in the individual character dictionary 7 with the feature values of “East”, “Kyo”, and “Me” from the feature extraction unit 4 respectively. The recognized character is output from the recognition result output unit 6 as a recognition result.

  FIG. 8A shows a configuration diagram of a conventional word recognition device 1.

  The word recognition device 1 includes an image input unit 2, a normalization unit 3, a feature extraction unit 4, a feature matching unit 5, a feature synthesis unit 8, a recognition result output unit 6, a word list unit 9, and an individual character dictionary 7.

  The image input unit 2 acquires an input image of handwritten characters using a scanner or the like. The normalization unit 3 aligns the size and position of the characters to be constant. The feature extraction unit 4 extracts feature amounts from the input image. The feature synthesizer 8 selects a word and extracts and synthesizes feature quantities of individual characters constituting the selected word from the individual character dictionary 7. The feature collating unit 5 collates the feature amount of the word synthesized by the feature synthesizing unit 8 with the feature amount of the input image extracted by the feature extracting unit 4. The word list unit 9 is composed of a plurality of word lists. The recognition result output unit 6 outputs a word having the most similar feature amount among the results collated by the feature collating unit 5 to a display or the like. The individual character dictionary 7 is composed of characters and their feature values.

  FIG. 8B shows an example of an address.

  It is assumed that characters such as prefectures are printed at specified coordinates. The image input unit 2 first detects a position where a character serving as a key of a prefecture is printed from the input image, and cuts out a character string “Tokyo” before that. The extracted character string is normalized by the normalization unit 3. For the normalized word, a feature amount is extracted by the feature extraction unit 4 and passed to the feature matching unit 5. The feature synthesis unit 8 selects a word list of prefectures in the word list unit 9. In the word list, “Hokkaido”, “Tokyo”, “Okinawa”, and the like are registered as words, and in the individual character dictionary 7, feature quantities of all characters are registered.

  First, the feature synthesis unit 8 first selects the word “North Sea” in accordance with the word list of prefectures, retrieves the feature quantities of “North” and “Sea” from the individual character dictionary 7, and obtains the feature of “North Sea”. Synthesize amount. The combination result and the feature amount of the input image are collated by the feature collating unit 5 to obtain a difference in feature amount between them. The same process is performed for all words in the word list. When the collation process is completed, those words having a small feature amount difference are output from the recognition result output unit 6 as a recognition result.

 In an apparatus that recognizes characters individually, it is difficult to accurately extract a character string in contact with the characters. For this reason, a word recognition method has been introduced in which contact characters are collectively recognized as words without being separated. However, in the method of recognizing the words in a lump without separating them into individual characters, since the word feature amount is extracted from the image of the word, it can be performed with high recognition if the characters are arranged at the same height. When the character string is tilted or the size of characters constituting the word is different and different, the recognition accuracy is low.

  Also, when synthesizing word feature quantities in the feature synthesizer of the word recognizer, the same partial character strings are included, such as “Dazai Uzuka” and “Dazai fence”, because they are synthesized based on individual character dictionaries. Since the “Dazai” portion is combined every time, the combining processing is wasted.

  Also, when the feature synthesizer of the word recognition device synthesizes the feature amount of the word, the feature dimension number differs from the feature amount of the input image due to extraction from the single-size individual character dictionary, and misreading It was a factor. In addition, leaning words were a factor in misreading.

  Further, in the feature matching unit of the word recognition device, misreading is likely to occur between the words containing the same character due to the feature amount of the same character. An object of the present invention is to provide (1) a word recognition device with improved recognition accuracy of a tilted word and a word of a different character size, and (2) word recognition having a feature synthesis method with high recognition speed and improved recognition accuracy. There are provided an apparatus and (3) a word recognition apparatus having a feature matching method that prevents misreading between words including the same character.

  A word recognition device for recognizing a word by collating a result of extracting a feature amount of a word from an input image with a result of synthesizing the feature amount of a word from the feature amount of each character of a dictionary, A dictionary for storing character and partial character string feature quantities and images of various character sizes, means for synthesizing word feature quantities from character and partial character string feature quantities based on the dictionary, and the dictionary And a feature synthesis unit having means for synthesizing a word image from a character image and extracting a feature amount.

  With this configuration, not only the characters that make up the word but also the feature amounts of the partial character strings with high appearance frequency are stored in advance as a dictionary, so that the speed of synthesizing the feature amounts of the words can be increased and different character sizes and partial characters can be used. Recognizing accuracy increases by having a column. Furthermore, since the feature amount of the tilted word can be extracted by combining the images, the recognition accuracy of the tilted word is increased.

  A word recognizing device having a feature synthesizing unit that includes a selection unit that selects from a dictionary a feature amount of a character with a predetermined word size and a feature amount of a partial character string with a size corresponding to the number of characters.

  With this configuration, the feature dimensions of the input image and the synthesized feature dimensions are the same by combining the feature quantities of the words with the same normalization size and a plurality of different character sizes according to the number of characters. Accuracy is improved.

  A word recognizing device having a feature synthesizing unit having conversion means for converting characters constituting a word extracted from a dictionary and features of a partial character string into characters of a predetermined size and features of the partial character string.

  With this configuration, since the feature quantities of words of different sizes can be synthesized based on the feature quantities of standard character size and partial character string size for individual character recognition, the recognition accuracy can be improved with a small dictionary capacity.

  In addition, the weights calculated by the complexity weighting means and the weighting means for weighting the feature amounts of the characters and partial character strings according to the complexity of the characters and partial character strings constituting the words extracted from the dictionary A word recognition device having a feature synthesizing unit that includes a character extracted from the dictionary and a conversion means for converting a partial character string into a character of a predetermined size and a partial character string.

  With this configuration, by increasing the feature dimensions of complex-shaped characters and reducing the feature dimensions of simple-shaped characters, recognition is performed according to the shape of the characters, and high-precision recognition is possible with a small dictionary capacity. It becomes possible.

  In addition, an inclination detection means for detecting the inclination of the word of the input image, an image constituting the word is extracted from the dictionary, the word image is arranged along the inclination detected by the inclination detection means, and the image composition and feature amount And a feature synthesis unit having means for extracting.

  With this configuration, it is possible to generate a feature amount of a word corresponding to the inclination of the input word, and the recognition accuracy of the word in the inclined character string is increased.

  Also, an input image weighting means for weighting the character constituting the word of the input image and the feature amount of the partial character string, a feature composition weighting means for weighting the character generated by the feature synthesis unit and the feature amount of the partial character string, and A word recognition device having a configuration further comprising a feature matching unit having a matching unit that matches a feature amount extracted by an input image weighting unit with a feature amount extracted by a feature synthesis weighting unit.

  With this configuration, the weight of the feature value of the same part is reduced and the feature value of another character or partial character string is increased for a specific character constituting the word or a word having the same specific partial character string. As a result, the difference between the feature amounts of similar words becomes clear, and the recognition accuracy increases.

 According to the present invention, it is possible to recognize a word even if the word is inclined, and it is also possible to recognize a character having a different character size.

  In addition, word recognition accuracy can be improved by synthesizing feature amounts by selecting a character size according to the number of characters. Furthermore, the recognition accuracy of the word is enhanced by extracting the feature amount by image synthesis even for the inclined word. Furthermore, by storing in advance the common partial character string or character features in the case of compound words, the processing at the time of feature synthesis can be speeded up.

  In addition, weighting the feature amount between words made up of the same characters or the same partial character strings can reduce misreading.

(Example)
The block diagram of the word recognition apparatus 1 of Example 1 is shown to Fig.1 (a).

  The image input unit 2, the individual character recognition unit 21, the word recognition unit 22, the certainty factor calculation unit 10, the certainty factor comparison unit 11, the recognition result output unit 6, and the individual character dictionary 7 are configured.

  The individual character recognition unit 21 includes a normalization unit 3, a feature extraction unit 4, and a feature matching unit 5.

  The word recognition unit 22 includes a normalization unit 3, a feature extraction unit 4, a feature matching unit 5, a feature synthesis unit 8, and a word list unit 9.

  The individual character recognition unit 21 and the word recognition unit 22 are the same as in the conventional example. The certainty factor calculating unit 10 calculates the certainty factor and the average of each character constituting the word from the individual character recognizing unit 21, and calculates the certainty factor of the word from the word recognizing unit 22. The certainty level comparison unit 11 compares the certainty level of the individual character recognition unit 21 and the certainty level of the word recognition unit 22. The certainty level means that the degree of similarity with the feature amount of the input image is high.

  The device is a combination of the individual character recognition process and the word batch recognition process, and the handwritten characters input by the image input unit 2 recognize characters and words at each recognition unit. And the certainty degree of the recognition result is calculated | required.

  The method of calculating the certainty factor is as follows.

First, a feature vector indicating the feature amount of the input image is extracted. Next, the individual character recognition unit 21 obtains a feature vector indicating the feature amount of the character in the dictionary, and obtains a distance value of a difference from the feature vector of the input image.
Repeat for all characters that make up a word.

  Further, the word recognition device 1 obtains a feature vector indicating the feature amount of the word synthesized by the feature synthesis unit 8 and obtains a distance value of a difference from the feature vector of the input image.

  Next, since the distribution of distance values is examined in advance using a large number of samples and normalized so that the range of distribution falls within the range of 0 to 1, the certainty factor calculation unit 10 calculates the distance values of the respective recognition units calculated. Convert to a distance value on the distribution. If the distribution is represented by f (X), the certainty factor R (d) when the distance value is d is represented by the following equation. The distance value d is 0 ≦ d ≦ 1, and when d = 1, the maximum similarity is considered.

  For example, in the case of a two-character word, R (d1) + R (d2) / 2, which is the average of the certainty factor for each character, is obtained from the individual character recognition unit 21. Next, from the word recognition unit 22, R (d3) is obtained from the certainty factor of the word. The certainty comparison unit compares the magnitudes. As a result, a result with a high certainty factor is output from the recognition result output unit 6 as a recognition result.

  The block diagram of the word recognition apparatus 1 of Example 2 is shown in FIG.

  The image input unit 2, the character interval detection unit 12, the threshold comparison unit 13, the individual character recognition unit 21, the word recognition unit 22, the recognition result output unit 6, and the individual character dictionary 7 are configured.

  The individual character recognition unit 21 includes a normalization unit 3, a feature extraction unit 4, and a feature matching unit 5.

  The word recognition unit 22 includes a normalization unit 3, a feature extraction unit 4, a feature matching unit 5, a feature synthesis unit 8, and a word list unit 9. The individual character recognition unit 21 and the word recognition unit 22 are the same as in the conventional example. The character spacing detector 12 detects the spacing between characters in the input image. The threshold comparison unit 13 compares the interval detected by the character interval detection unit 12 with a threshold.

  The process of the second embodiment is used when a high processing speed is required because it takes a long time to pass two recognition processes. First, the character spacing of the input word is detected, and the two recognition processes are used properly depending on whether the character spacing is larger or smaller than the threshold.

  The operation will be described with reference to FIG.

  If the image input from the image input unit 2 is Wakayama, the character interval detection unit 12 detects the character interval. For example, a circumscribed rectangle of a character is obtained from connected black pixels, and a space between the circumscribed rectangles is detected as a character interval. If the detection result is smaller than the threshold, the word recognition unit 22 performs processing because there is a high possibility that the characters are close to each other. If the detection result is larger than the threshold value, it is determined that they are not close to each other, and processing is performed by the individual character recognition unit 21 in units of individual characters.

  The block diagram of the word recognition apparatus 1 of Example 3 is shown in FIG.

The word recognition device 1 includes an image input unit 2, a normalization unit 3, a feature extraction unit 4, and a recognition processing unit 17.
The recognition processing unit 17 includes a feature matching unit 5, a feature synthesis unit 8, a recognition result output unit 6, a word list unit 9, an individual character dictionary 7, and a word dictionary 16.

  The image input unit 2 obtains an input image of handwritten characters from a scanner or the like and detects the inclination of the word. The normalization unit 3 aligns the size and position of the characters to be constant. The feature extraction unit 4 extracts feature amounts from the input image. The feature synthesizer 8 selects a word, extracts feature amounts of individual characters constituting the selected word from the individual character dictionary 7, and extracts feature amounts of partial character strings constituting the word from the word dictionary 16. Synthesize. Also, character, partial character string size conversion, complexity conversion, inclination processing, and the like are performed. The feature matching unit 5 matches the feature amount of the word synthesized by the feature synthesis unit 8 with the feature amount of the input image extracted by the feature extraction unit 4. The word list unit 9 is composed of a plurality of word lists. The recognition result output unit 6 outputs a word having the most similar feature amount among the results collated by the feature collating unit 5 to a display or the like. The individual character dictionary 7 is composed of characters, their feature values, and their images. The word dictionary 16 is composed of a partial character string common to compound words and its feature amount.

  The operation of the word recognition device 1 will be described with reference to FIGS.

  First, the case where “Tokyo” is input as an input image will be described with reference to the address example of FIG.

  It is assumed that characters such as prefectures are printed at specified coordinates. The image input unit 2 first detects a position where a character to be a key of a prefecture is printed from the input image, and cuts out a character string before that. Next, the connected black pixels are extracted to obtain a rectangle that circumscribes them. Set a straight line for the center of all circumscribed rectangles obtained. Then, the inclination is set as the inclination of the word and compared with a predetermined threshold value.

The description will be made separately for the case of slope <threshold and the case of slope ≧ threshold.
(1) Inclination <threshold value When the normalization unit 3 completes normalization to, for example, 64 × 64 pixels with respect to the input character string “Tokyo”, the feature extraction unit 4 sets “Tokyo” A feature amount of the input image is extracted. In the word list of prefectures, words of prefectures such as North Sea, Tokyo, and Okinawa are registered, and in the individual character dictionary 7, feature quantities of all characters are registered.

  Next, the flow of processing of the recognition processing unit 17 is shown in FIG.

  Although the description is for individual characters, the same applies to partial character strings.

  First, the feature synthesis unit 8 first searches for the word “North Sea” according to the word list (step S11). Next, the feature amount of “north” and the feature amount of “sea” are extracted from the individual character dictionary 7 to synthesize the feature amounts. There are three types of methods. Feature synthesis processing is performed so that the number of dimensions of the feature vector after feature synthesis is equal to the number of dimensions of normalization of the input image.

  A case where a mesh feature is used as the feature is shown. The mesh feature amount is obtained by dividing the number of black pixels in each small rectangle by the small rectangle size when the image is divided into m × n meshes. For example, if the normalized size is 64 × 64 pixels, the size of the small rectangle divided into 8 × 8 mesh is 8 × 8. If there are 32 black pixels in a small rectangle, the mesh feature amount is 32/64 = 0.5. Since the input image is normalized by 64 × 64 pixels and 8 × 8 meshes and features are extracted, the feature synthesis result is also synthesized to be 8 × 8 meshes by 64 × 64 pixels.

  The first method is feature synthesis by the individual character dictionary 7 having a reduced character size (step S121 in FIG. 4). An explanatory diagram is shown in FIG.

  Used for high-speed dictionary memory capacity, such as dedicated OCR machines. With respect to one character and partial character string, the individual character dictionary 7 has feature amounts extracted from a plurality of character sizes. An example using an individual character dictionary 7 having reduced characters of ½ size is shown. A half-size reduced character is 8 × 4 mesh.

  Based on the word list of prefectures, feature sizes of “North” and “1/2” of “Hokkaido” are extracted from the individual character dictionary 7 and synthesized to form an 8 × 8 mesh size. The feature amount of the word “North Sea” is set (step S13).

  The word feature value is compared with the feature value of the input image and the difference is stored (step S14 in FIG. 4).

  The second method is feature synthesis of the character size conversion method (step S122 in FIG. 4). It is used for devices such as portable information terminals that cannot have a large memory capacity for a dictionary. This method is a method of generating feature amounts of various character sizes by applying conversion coefficients to standard character size feature amounts and synthesizing word feature amounts. An explanatory diagram of the feature value composition is shown in FIG.

  A character size feature amount reduced by applying a conversion coefficient to a standard character size feature amount is synthesized.

  For example, since the standard character size feature amount of 64 × 64 pixels is 8 × 8 mesh, by multiplying the standard character size of “north” by 8 × 4 conversion coefficients, The feature amount of the character “north” is obtained. Further, by multiplying the standard character size of “Umi” by a conversion factor of 8 × 4 in the vertical direction, the characteristic amount of the character of “Umi” of 1/2 size can be obtained. The combination of the two is used as the word feature of “North Sea” (step S13 in FIG. 4).

  The word feature value is compared with the feature value of the input image and the difference is stored (step S14 in FIG. 4).

  The third method is feature synthesis of the complexity conversion method (step S123 in FIG. 4). It is used for a device such as a portable information terminal, which cannot take up a large memory capacity for a dictionary and wants high recognition accuracy. In order to perform highly accurate processing, first, the complexity of characters constituting a word is examined. As the complexity, for example, FIG. 5C shows an explanatory diagram for calculating the linear density using the vertical linear density.

  This is a method of synthesizing word feature amounts by weighting the feature amounts according to the ratio of the sum of the line densities of the respective characters.

  For example, for the word “North Sea”, if the line density of “North” is 30 and the line density of “Sea” is 50, then the character size at this time is 64 × 64 pixels, and it is divided into 8 × 8 meshes. Therefore, the feature quantity of “north” is 8 × 30 / (30 + 50) = 3 for the number of horizontal meshes, and the feature quantity of “sea” is 8 × 50 / (30 + 50) = for the number of horizontal meshes. 5 Then, the feature amount of the character “north” is obtained by multiplying the standard character size “north” by a conversion factor of 8 × 3. Also, the character quantity of “sea” can be obtained by multiplying the standard character size of “sea” by a conversion factor of 8 × 5. The combination of the two is used as the word feature of “North Sea” (step S13 in FIG. 4).

The word feature value is compared with the feature value of the input image and the difference is stored (step S14 in FIG. 4).
These processes are performed for all the words, and finally, a feature value having a minimum difference between the feature value of the input image and the feature value from the feature synthesis unit 8 is output as a recognition result (S15 in FIG. 4). Step).

Although not shown in the figure, there are many compound words including the same partial character string in the word, such as Taiko-Matsuka and Taikogaki. Therefore, instead of synthesizing “Dazai” from “Da” and “秦” every time the feature amount is synthesized, for “Dazai” in advance, for example, a feature amount of 1/2 character size is extracted from the word dictionary 16. Then, from the individual character dictionary 7, the feature amount of a character of “¼” size “horse” +1/4 size “mound” is extracted, and the word feature amount is derived from the feature amount of “dazai” + “horse” + “mound”. Is a method to synthesize.
(2) When the slope is equal to or greater than the threshold value.

  FIG. 6 shows an explanatory diagram of the recognition processing unit 17 in the case of the inclination degree ≧ the threshold value.

  FIG. 6A shows a flow of processing, and FIG. 6B shows an example of an address.

  After extracting the character string of the input image “Tokyo” in the address example, the normalization unit 3 performs normalization as an inclined word, and then the feature extraction unit 4 performs feature extraction. Next, the word “North Sea” is acquired from the word list of prefectures, and images of “North” and “Sea” are acquired from the individual character dictionary 7 using the characters constituting the word as a key (step S51).

The acquired image is arranged along the inclination degree θ. (Step S52)
Next, the image is normalized to the same size as the normalized size of the input image (step S53).

  Feature extraction is performed based on the normalized word image (step S54).

  The extracted feature value is compared with the feature value extracted from the input image, and the difference is stored (step S55).

  The above processing is performed for all words in the word list. Among all the words, the word having the smallest difference is output from the recognition output unit (step S56).

  Although not shown, in the feature matching unit 5, for example, when the input word is an address and it is known in advance that it is one of “Dazai OO”, the “Dazai” part is It is not an effective feature for matching. Therefore, by reducing or masking the weight of the feature amount of this part and increasing the weight of the other components, or performing matching without changing the weight, the difference in the feature amount of similar words is clarified. This increases the recognition accuracy.

For example, if the input image is “Dazai Mazuka”, this character string is cut out, the common character part “Dazai” is masked, and feature extraction is performed only on the remaining character “Matsuka” part. Next, “Dazai XX” is extracted from the word list. For example, if the extracted word is “Dazai fence”, “Dazai” is masked.
Then, the remaining “Kakiuchi” features are synthesized. Then, the feature amount of the input image is compared with the feature amount obtained by feature synthesis. This collation is performed for all compound words including words in the word list, and the word with the smallest feature amount difference is specified as the input word.

Configuration diagram of word recognition apparatus of embodiment 1 Configuration diagram of word recognition device of embodiment 2 Configuration diagram of word recognition device of embodiment 3 Explanatory drawing of the recognition process part at the time of the inclination of Example 3 <threshold value Explanatory drawing of the feature-value composition of Example 3 Explanatory drawing of the recognition process part at the time of the inclination degree of Example 3> threshold value Configuration diagram of a conventional individual character recognition device Configuration diagram of a conventional word recognition device

Explanation of symbols

DESCRIPTION OF SYMBOLS 1 Word recognition apparatus 2 Image input part 3 Normalization part 4 Feature extraction part 5 Feature collation part 6 Recognition result output part 7 Individual character dictionary 8 Feature synthesis part 9 Word list part 10 Confidence degree calculation part 11 Confidence degree comparison part 21 Individual character Recognition unit 22 Word recognition unit

Claims (6)

A word list section for storing words;
An image input unit for acquiring an input image of a word;
An individual character dictionary that pre-stores feature quantities of each character constituting a word;
A feature extraction unit that normalizes the input image and extracts a feature amount;
Extracting a word from the word list part, and comparing the feature amount of each character constituting the extracted word with the feature amount of the normalized input image, a predetermined reduction conversion according to the number of characters constituting the word A feature synthesizer having conversion means for performing the above and means for synthesizing the feature values of each converted character ;
A feature matching unit that matches the feature value obtained from the input image with the synthesized feature value;
A word recognition apparatus comprising: a recognition result output unit that outputs a recognition result having a minimum difference as a result of collating all the words in the word list unit . The individual character dictionary has a feature amount of a plurality of reduced-size characters generated according to the number of characters constituting the word,
Conversion means
2. The word recognition apparatus according to claim 1 , wherein, for each character constituting a word extracted from the word list portion, a feature amount of a reduced size character corresponding to the number of characters is acquired from an individual character dictionary . Conversion means
The feature amount of each character having a reduced size is obtained by applying a predetermined conversion coefficient corresponding to the number of characters constituting the word to the feature amount of each character constituting the word extracted from the word list portion. The word recognition device according to 1. Conversion means
Means for calculating the complexity by the line density of each character constituting the word extracted from the word list, weighting each character based on the ratio to the total sum of the obtained complexity, and the feature amount of each character constituting the word Means for obtaining the characteristic amount of each character by assigning the obtained weight;
Word recognition apparatus according to claim 1, characterized in that it has. A word dictionary that pre-stores feature quantities of character strings constituting words;
When some or all of the character strings extracted from the word list part are stored in the word dictionary, the feature amount is extracted and reduced according to the number of characters, and for each remaining character, 2. The word according to claim 1, further comprising: a feature synthesizer that extracts a feature amount of each character from the individual character dictionary, performs reduction conversion according to the number of characters, and synthesizes each converted feature amount. Recognition device. A word list section for storing words;
An individual character dictionary that stores an image corresponding to each character constituting the word;
An image input unit that acquires an input image of a word, and acquires an inclination of the word from an inclination of a straight line with respect to a center of a circumscribed rectangle of the acquired input image;
A feature extraction unit that normalizes the input image and extracts a feature amount;
When the inclination exceeds a threshold, a word is extracted from the word list portion, an image corresponding to each character constituting the extracted word is extracted from an individual character dictionary, and the extracted image is acquired. A feature synthesizer that arranges according to the inclination and extracts a feature amount by normalizing to the same size as the normalized size of the input image;
A feature matching unit that matches the feature value obtained from the input image with the synthesized feature value;
A word recognition apparatus comprising: a recognition result output unit that outputs a recognition result having a minimum difference as a result of collating all the words in the word list unit .

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