Glory Kogyo Kabushiki Kaisha

Shoji Miki

Hiroki Tsubota

Kazuhiko Ohnishi

Hirofumi Kameyama

Katsuji Uosaki

Yasuo Fujita

Kenta Hori

Masanori Yokota

A check processing equipment having an optical amount character recognizing function for a proof machine used at a bank. A received amount can be corrected on an image screen, and it enables the efficient processing for certifying operation. A document capturing unit converts the data on the document into an image data and transfers the image data to a frame memory of recognizing unit. A recognizing unit compresses the image data through a recognizing/determining section and also carries out CAR with the attribute on an E13B code line data. A work station stores the compressed image data, the code line data, and the CAR result in a hard disk and it also displays an extended image and implements the proof of a whole batch through key-in operation for handling unrecognizable data and exceptional error data. An encoder unit performs encode printing and endorse printing on proved documents while verifying the code lines thereof.

Check processing equipment

A plurality of recognition candidates are obtained through continuing a segmentation to a numeral recognition realized by segments, and an optimum one is selected from among the candidates by means of connection information, thereby preventing continuous numerals from being recognized erroneously. A method of correctly recognizing the handwritten characters, especially numerals written in checks includes the steps of: performing a line drawing analysis is of the character set to be recognized in order to extract a characteristic of the arrangement of blocks, proposing a hypothesis about the monetary unit type, and finally verifying the above hypothesis to display the result of the character recognition. Further, this method can surely distinguish handwritten characters from a bar which is either printed for entry of characters or handwritten, and eliminate the bar from the object of character recognition to extract the characters alone. In detecting the bar, six formulas for under-bar, middle-bar, upper-bar, inclined bar, inclined under-bar and inclined upper-bar relative to the characters are used to determine certainty factor of a bar, and the bar having the highest linearity is extracted.

Character recognizing system

A letter pattern recognition method preprocesses each character pattern to effect characteristic extraction based on elements such as a loop, a straight line and an arc and is adapted to compare the character pattern with a preregistered classification code after the normalization thereof, the character pattern being subjected to balance and stroke checks if necessary as a result of the comparison. Notwithstanding this, if the character is not defined, the character pattern is identified by means of detail classifications such as those of a divergent mode of the arc, and a length ratio of the elements and the like.

Recognition method for letter patterns

A method for identifying a plurality of handwritten connected figures includes identifying and prioritizing branches of the connected figures. A first recognition block is formed extending a predetermined distance from one end of the connected figures. Branches having the lowest priority within the first recognition block are erased until a recognizable figure is obtained within the first recognition block. Then, a second recognition block is formed extending the predetermined distance from the recognizable figure obtained in the first recognition block. As with the first recognition block, the branches having the lowest priority within the second recognition block are erased until a recognizable figure is obtained within the second recognition block.

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Himeji, Japan

Shoji Miki