Information recognition with transmission between character segmenting and recognition processors

An information recognition apparatus includes a first identification unit for performing first identification processing and a second identification unit for performing second identification processing, as units for identifying information. In this apparatus, the first identification unit and the second identification unit can perform identification processing in parallel. An information recognition method includes performing first identification processing and second identification processing as processing for identifying information. In this method, the first identification processing and the second identification processing can be performed in parallel.

BACKGROUND OF THE INVENTION 
1. Field of the Invention 
This invention relates to a recognition apparatus and method for 
recognizing input information. 
2. Description of the Prior Art 
There are techniques wherein a host computer is connected to a recognition 
apparatus via a transmission channel, the host computer performs input of 
image information and display/editing of the results of recognition, and 
the recognition apparatus performs segmenting of character and symbol 
images from the image information, and recognition of segmented characters 
and symbols. 
The above-described conventional techniques, however, have the following 
disadvantages, since image information is transmitted from the host 
computer, to which an image has been input, to the recognition apparatus 
via the transmission channel, and the recognition apparatus performs 
segmenting of characters/symbols from the image information. 
(1) The recognition apparatus must include a memory having a large capacity 
in order to store image information for performing segmenting of 
characters/symbols. 
(2) A long transmission time is needed because of the large amount of data 
included in the image information transmitted from the host computer to 
the recognition apparatus. 
(3) Since segmenting of characters/symbols is performed within the 
recognition apparatus, a high-speed processor is needed in order to 
perform the segmenting processing. 
In performing character recognition on a text in a language having many 
kinds of characters, which are to be distinguished and recognized such as 
the Japanese language, there are techniques wherein processing is 
performed in parallel using a plurality of identical processing units in 
order to increase the speed of the extraction of characteristics, and 
recognition processing. 
An explanation will new be provided of such a technique with reference to 
FIG. 18, In FIG. 18, an input image is separately stored in storage units 
1, 2 and 3 (elements 91-93). Subsequently, three characteristics 
extraction units 1, 2 and 3 (elements 94-96) extract characteristics data 
in parallel, and input the extracted data to a characteristics data 
storage unit 97. Subsequently, three identification units 1, 2 and 3 
(elements 98-100) perform identification in parallel, and sorting is 
finally performed in element 101. 
The above-described conventional techniques, however, have the disadvantage 
of relatively high cost, since the technique must include a plurality of 
identical processing units. 
Recognition algorithms used in character recognition have included 
techniques emphasizing the extraction of characteristics wherein 
complicated processing is performed at the stage of the extraction of 
characteristics, and the identification function, applied to the extracted 
characteristics to effect actual recognition of the character, has a 
simple form. 
The above-described conventional techniques, however, have the disadvantage 
that a very long recognition time is needed because of the complicated 
processing at the stage of the extraction of characteristics, and 
complicated dedicated circuitry is needed in order to execute the 
processing at a high speed. 
Among techniques emphasizing the identification function are techniques 
wherein the degree of similarity is calculated neglecting higher-order 
terms of eigenvalues, and only main components are analyzed, to shorten 
the time in recognition processing. 
The above-described conventional techniques, however, have the disadvantage 
of decreasing the rate of recognition unless a particular countermeasure, 
such as increasing the number of dimensions of the characteristic vector 
or the like, is taken, since these techniques emphasize the recognition 
function based on the analysis of main components. 
SUMMARY OF THE INVENTION 
The present invention has the effect that the time for recognition 
processing can be shortened by providing an information recognition 
apparatus comprising first identification means for performing first 
identification processing and second identification means for performing 
second identification processing as means for identifying information, 
wherein the first identification means and the second identification means 
can perform identification processing in parallel. 
The present invention also has the effect that the efficiency of a 
transmission channel is increased and the time for information recognition 
can be shortened by providing an information recognition apparatus 
comprising a plurality of information processing means, wherein the 
transmission of information from first processing means to second 
processing means and the transmission of information from the second 
processing means to third processing means can be performed in parallel. 
More preferably, the present invention has the effect that the transmission 
of information can be adapted to a situation, and the efficiency of the 
transmission channel is further increased by the information recognition 
apparatus further comprising information amount control means for 
controlling the information to be transmitted so as to permit transmission 
of an arbitrary amount of information. 
The present invention has the effect that the cost of a recognition 
apparatus can be reduced since the recognition apparatus need not have 
means for segmenting information, by providing a recognition system 
comprising a processing apparatus for inputting of information and for 
performing display and editing thereof, and recognition apparatus for 
recognizing input information, wherein the processing apparatus comprises 
segmenting means for segmenting desired information from the input 
information, and transmission means for transmitting the segmented desired 
information to the recognition apparatus.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS 
In most cases, a character recognition apparatus is used not by itself, but 
combined with a host computer as a whole system. The role of the host 
computer as an interface with a user is of great importance. 
The functions necessary for a character recognition apparatus are 
segmenting of characters, extraction of characteristics, gross 
classification, detailed classification, and postprocessing. An 
explanation will now be provided of a method of performing segmenting of 
characters and postprocessing. 
The use of this method has the advantage that the character recognition 
apparatus need not include a memory for receving image information. 
Furthermore, by dividing the processing between the host computer and the 
character recognition apparatus, the performance speed of the processing 
can be increased, and the capacity of a memory incorporated in the 
character recognition apparatus can be minimized. 
An explanation will now be provided of the relationship between processing 
in the host computer and processing in the character recognition apparatus 
for performing character recognition, with reference to FIG. 1. 
First, image data read by a scanner are transmitted to a host computer 10. 
The host computer 10 segments characters (in element 11), and transmits 
image data for a plurality of characters to a main body 20 of an OCR 
apparatus. After performing extraction of characteristics in element 21, 
gross classification in element 22 and detailed classification in element 
23, the results of recognition are sent back to the host computer 10. 
Postprocessing is performed in element 12 in the host computer 10. By 
rewriting a device driver in well-known manner, the OCR apparatus 20 can 
be connected to another computer. 
A more detailed explanation of the processing shown in FIG. 1 will now be 
provided. 
FIG. 2 is the basic configuration of the present invention. An image 
scanner 31, a FAX (facsimile) 32 and an image file 33 are image input 
apparatuses for a host machine 34. The host machine 34 comprises an image 
memory 35 for storing image inputs from the group of input apparatuses and 
outputting them to a display unit 36, a keyboard 37, serving as an input 
means for assigning areas in the image memory 35 and for 
editing/correcting the results of recognition, a pointing device 38, such 
as a mouse (trademark), and a character/symbol segmenting unit 39 for 
segmenting characters and symbols. The host machine 34 further comprises a 
communication control unit 40 for controlling communication with a 
recognition apparatus, a DMA (direct memory access) memory 41 for 
performing communication with the recognition apparatus by DMA transfer, a 
DMAC (direct memory access controller) 42 for controlling DMA, and a CPU 
43 for controlling the host machine 34. 
A recognition apparatus 44 comprises a recognition unit 45 for performing 
the extraction and identification of characteristics of 
characters/symbols, and a communication control unit 46 for performing 
communication control with the host machine 34. The recognition apparatus 
44 further comprises an Rx memory 47 and an Rx memory 48 for performing 
DMA transfer of character/symbol image blocks from the host machine 34 and 
storing them, a Tx memory 49 for storing the results of recognition and 
performing DMA transfer of the stored results to the host machine 34, a 
DMAC 50 for controlling DMA transfer, and a CPU 51 for controlling the 
entire recognition apparatus 44. 
Transmission channel 52 connects the recognition apparatus 44 to the host 
machine 34. 
FIG. 3 is a flowchart showing the flow of processing according to an 
embodiment of the present invention performed in the CPUs 43 and 51. 
Image information (an original image) is input from any of the input 
devices 31, 32 and 33 to the image memory 35 of the host machine 34 (S1). 
The original image stored in the image memory 35 is output to the display 
unit 36, and the area whose contents are to be recognized is assigned 
using the keyboard 37 or the mouse 38 (S2). 
Character/symbol image blocks in the area to be recognized assigned by the 
character/symbol segmenting unit 39 are segmented (S3). 
The segmented character/symbol image blocks are transmitted to the 
recognition apparatus 44 via the communication control unit 40. At that 
time, the number of character/symbol image blocks to be transmitted from 
the host machine 34 to the recognition apparatus 44 is determined (S4). 
The character/symbol image blocks transmitted to the recognition apparatus 
44 are stored in the Rx memories 47 and 48, and are then transmitted to 
the recognition unit 45 for extraction of characteristics and the 
identification calculation for each of the character/symbol image blocks. 
As a result, candidate characters (at least one), serving as code 
information for characters/symbols, are obtained and are stored in the Tx 
memory 49 (S5). 
The code information for characters/symbols, stored in the Tx memory 49, is 
transmitted to the host machine 34 via the communication control unit 46. 
At that time, the amount of the code information for characters/symbols to 
be transmitted from the recognition apparatus 44 to the host machine 34 
(e.g., the number of such characters/symbols or any other convenient 
measure of the amount of information to be transmitted) is predetermined 
(S6). 
Furthermore, the result of the identification calculation performed at step 
S5 is added to the information which has been stored in the Tx memory 49 
and transmitted at that time. 
The host machine 34 displays the code information transmitted from the 
recognition apparatus 44 on the display unit 36 as the results of 
recognition, and corrects/edits the code information in response to 
instructions input by the operator using the keyboard 37 or the mouse 38 
(S7). 
FIG. 6 shows the time sequence of events in the recognition of 
characters/symbols on three lines enclosed with a frame on a display on 
the display unit 36 as shown in FIG. 4. A detailed explanation will now be 
provided of processing according to the time sequence shown in FIG. 6. In 
the present embodiment, a case is shown wherein segmenting of 
characters/symbols in the host machine 34 is performed in units of a line, 
and transmission is also performed in units of a line. 
First, image blocks in units of a character/symbol enclosed with broken 
lines on the first line, as shown in FIG. 5, are segmented, from the 
recognition area having three lines enclosed with the frame on the display 
shown in FIG. 4 by the character/symbol segmenting unit 39 (L1). When 
segmenting for one line has been completed, the contents (for 5 characters 
in the illustrated example) are stored in the DMA memory 41, and are 
transferred from the DMA memory 41 to the recognition apparatus 44 via the 
communication control unit 40 and the transmission channel 52 under the 
control of the DMAC 42 (Rx1). While the contents are transferred, the CPU 
43 performs segmenting of characters/symbols on the second line using the 
character/symbol segmenting unit 39. 
The recognition apparatus 44 stores the transferred contents (L1) in the Rx 
memory 47 from the communication control unit 46 by DMA transfer via the 
DMAC 50. The recognition unit 45 sequentially recognizes every 
character/symbol image block from the character/symbol image blocks stored 
in the Rx memory 47, and stores the results of recognition in the Tx 
memory 49 (L1-1-L1-5). When the recognition for one line has been 
completed, the results are transmitted from the Tx memory 49 to the host 
machine 34 via the communication control unit 46 and the transmission 
channel 52 by DMA transfer (Tx1). 
The host machine 34 stores the results of recognition in the DMA memory 41 
via the communication control unit 40 by DMA transfer, and output the 
contents on the display unit 36 (Disp1). 
When the host machine 34 has completed segmenting of characters/symbols on 
the second line (L2), the character/symbol image blocks in L2 are 
transferred to the recognition apparatus 44, as in the transfer operation 
in L1. The recognition apparatus 44 stores the transferred 
character/symbol image blocks in L2 in the Rx memory 48. After the 
completion of the recognition in L1, the recognition apparatus 44 
instantaneously starts the recognition of the contents stored in the Rx 
memory 48 (Rx2, L2-1-L2-3). 
In the same manner, recognition continues until the third line is 
completed, as shown in FIG. 6. 
FIG. 7 shows a transmission format to be used in transmission from the host 
machine 32 to the recognition apparatus 44. The header indicates the size 
of a 1-character or 1-symbol image block. Symbols Lx-1-Lx-n represent the 
contents of respective character/symbol image blocks. 
FIGS. 8 and 9 show two examples of transmission formats to be used in 
transmission from the recognition apparatus 44 to the host machine 34. 
FIG. 8 shows candidate-character information. In FIG. 9, the results of 
calculation are added to the candidate-character information shown in FIG. 
8. Although, in the present embodiment, a case is illustrated wherein the 
number of candidate characters is four, the number is not limited to four, 
The header indicates the size of total capacity. Symbols K-1 through K-4 
represent candidate-character codes, and symbols S-1 through S-4 represent 
the results of calculation in the recognition unit 45 which correspond to 
the respective candidate characters. 
Next, an explanation will be provided of a second embodiment with reference 
to FIG. 14 wherein recognition speed is increased in a character 
recognition apparatus by providing a general-purpose CPU, a DSP (digital 
signal processor) and two dedicated LSIs (large-scale integrated 
circuits), and performing pipeline processing, the processing being shared 
by these components. 
A recognition board in an OCR apparatus comprises a communication control 
unit and a recognition unit. The communication control unit controls the 
transmission/reception of data between a host computer and the recognition 
unit, and incorporates an SCSI (small computer system interface) 
controller 75 for communication with the host computer. The communication 
control unit also includes a communicaton buffer 73, a DMA controller 74 
and a PROM (programmable read-only memory) 72. 
The recognition unit performs extraction of characteristics, gross 
classification and detailed classification. Two kinds of dedicated LSIs 
are mounted on the recognition unit. One is a dedicated LSI for extracting 
characteristics of a character to be recognized. Another one is a 
dedicated LSI for roughly selecting candidate characters. The recognition 
unit also includes a DSP 81, a gross classification dictionary 85, a 
detailed classification dictionary 86 and a PROM 82. The recognition unit 
further includes an internal buffer 76 for data transmission to the 
communication control unit. The internal buffer memory has a dual-port 
configuration. 
CPU 71, the DSP 81 and the two kinds of dedicated LSIs perform different 
processing from one another. The CPU 71 transfers image data from the 
communication buffer 73 to the internal buffer 76. The DSP 81 extracts 
characteristics of a character to be recognized by controlling the 
characteristics extraction LSI 83, and executes processing to narrowly 
select candidates from among those presented as a result of the processing 
by the gross classification LSI 84. 
In detailed classification, the distance to a standard pattern is 
calculated using a pseudo-Bayes identification function for 48 candidates 
for recognition selected by the gross classification LSI 48, and the 8 
most similar characters are selected as final candidate characters. In 
order to calculate the pseudo-Bayes identification function for one 
candidate for recognition, 519 product/sum calculations are needed. 
In FIG. 1, the OCR apparatus 20 is operated from the side of the host 
computer 10. If an application tool for that purpose is used, the user can 
assign an area to be recognized by the OCR apparatus 20 for image data 
read by an image reader, using the mouse. Furthermore, postprocessing of a 
recognized document is executed using this tool. For example, a function 
of performing reversal display of uncertain characters among the result of 
recognition by the OCR apparatus 20 is provided. For each such uncertain 
character subjected to reversal display, a character which the user 
considers suitable can be selected from among other candidate characters 
for that block. 
FIGS. 10-13 illustrate the present embodiment. FIG. 1 is most clearly shows 
the overall structure of the present invention. In FIG. 10, there is shown 
a transmission channel 69 for connecting the apparatus to the computer. A 
communication control unit 60 performs communication of data with the 
outside via the transmission channel 69. A first storage unit 64 stores a 
plurality of character images received from the transmission channel 69. A 
second storage unit 65 stores one character image. A CPU 61 sequentially 
transfers character images one by one from the first storage unit 64 to 
the second storage unit 65, and controls the entire apparatus. A third 
storage unit 66 stores characteristics data. A characteristics extraction 
unit 62 reads the character image stored in the second storage unit 65, 
extracts characteristics of the read character image, and stores 
characteristics data in the third storage unit 66. A recognition 
dictionary 68 stores reference patterns. An identification unit 63 
performs character recognition by reading characteristics data stored in 
the third storage unit 66 and calculating the distance to a standard 
pattern stored in the recognition dictionary 68, and stores the results of 
recognition in the storage unit 67 for transmission. FIG. 11 is a time 
chart illustrating operation of the present embodiment. FIG. 12 is a 
flowchart of processing of the present embodiment. An explanation will now 
be provided with reference to FIG. 12. 
First, before executing all processing, the contents in the respective 
storage units 64, 65, 66 and 67 are initialized at step S30. Subsequently, 
at step S31, a plurality of (M) character images and data as shown in FIG. 
13, such as information relating to the size of the character images and 
the like, are transmitted from a computer (not shown) to the communication 
control unit 60 via the transmission channel 69, and are stored in the 
first storage unit 64. Steps S32-S35 are looped for the number of the 
character images (M times). At step S32, if the number i of the loops 
already performed is less than M, the process proceeds to step S33, where 
the CPU 61 accesses the first storage unit 64 to read an i-th character 
image i, and transfers it to the second storage unit 65. At step S34, the 
characteristics extraction unit 62 reads data of the chracter image i from 
the second storage unit 65, performs image processing, and stores 
characteristics data in the third storage unit 66. Any suitable known 
technique (for example, a technique shown in Japanese Patent Application 
Public Disclosure (Kokai) No. 59-177684 (1984)) may be used as the method 
of extracting characteristics. In the referenced technique, for example, 
characteristics data are represented by characteristic vectors comprising 
a weighted directional index histogram. When step S34 has been completed, 
two kinds of processing are executed in parallel. The first processing is 
identification processing relating to the character image i at step S35. 
The second processing is image transfer of character image i+1 at step S33 
(if the loop has not been repeated M times at step S32). Timings for such 
parallel processing are shown in FIG. 11. In the identification processing 
at step S35, the distance between a standard pattern and the 
above-described characteristic vector is calculated using a pseudo-Bayes 
identification expression, as shown in the above-cited Japanese Patent 
Application Public Disclosure (Kokai) No. 59-177684 (1984), identification 
is performed while sorting characters in the order of the distance, and 
the results of recognition (character codes for the 8 most significant 
characters having small distances, and the calculated values of those 
distances) are stored in the storage unit 67 for transmission. While 
executing the identification processing relating to the character image i, 
the image transfer of the character image i+1 from the first storage unit 
64 to the second storage unit 65 is executed in parallel. This operation 
is possible in the present invention, since the image transfer and the 
access of the characteristics extraction unit 62 to the second storage 
unit 65 are not overlapped in time (see FIG. 11). 
If the loop from step S32 to step S35 has been repeated M times, the 
process proceeds to step S36, where the results of character recognition 
for the M characters (the contents of the storage unit 67 for 
transmission) are transmitted from the communication control unit 60 to 
the transmission channel 69. 
Next, an explanation will be provided of the character recognition 
apparatus connected to the computer via the transmission channel with 
reference to FIGS. 14-17. 
In FIG. 14, the CPU 71 performs image transfer and the control of the 
entire apparatus. The PROM 72 stores the processing procedure of the CPU 
71. The communication buffer 73 stores character images received from the 
outside via the transmission channel. The DMA controller 74 performs the 
transmission/reception of data between the transmission channel and the 
communication buffer 73 without passing through the CPU 71. The 
communication control unit 75 controls the protocol of the transmission 
channel. There are also shown data bus 70 for the CPU 71, the internal 
buffer 76, the DSP 81, data bus 80 for the DSP 81, the PROM 82 for storing 
the processing procedure of the DSP 81, the first characteristics 
extraction unit 83, the gross classification unit 84 incorporating an 
arithmetic unit, the dictionary 85 for gross classification, and the 
dictionary 86 for detailed classification. FIG. 15 is a timing chart of 
the present embodiment. FIG. 16 is a flowchart of the embodiment. The 
operation of the apparatus will now be explained in accordance with the 
flowchart shown in FIG. 16. 
The present apparatus is connected to the computer via the transmission 
channel. The computer transmits recognition commands or other commands 
(status requests and the like) to the apparatus. At step S71, a command is 
analyzed. If the command is a recognition command, the following 
recognition processing is performed. If the command is a command other 
than the recognition command, respective processing (which is to be 
carried out using techniques known to those of ordinary skill, and is 
therefore not disclosed here in detail) is performed in accordance with 
the command. At step S72, character image data (M character images) 
accompanying the recognition command are input to the communication buffer 
73 through the communication control unit 75 by the DMA controller 74. The 
format of the character image data is the same as that shown in FIG. 13. 
Subsequently, the CPU 71 initializes the memories and the like before 
recognition (S73). Processing from step S75 to step S79 is then looped a 
number of tines equal to the number of the character images (M times). 
Step S74 is a step to determine whether or not the loop has been repeated 
M times. If the number of repeated loops is less than M at step S74 (the 
loop is assumed to be the i-th loop), the process proceeds to step S75, 
where the CPU 71 transfers the i-th character image stored in the 
communication buffer 73 to the internal buffer 76 (processing Ai in FIG. 
15). Subsequently, at step S76, the DSP 81 controls the first 
characteristics extraction unit 83 to extract characteristics of the i-th 
character image while accessing the internal buffer 76. In this process, 
the DSP 81 supposes a predetermined size, for example a 63 pixel.times.63 
pixel character image (a normalized character image.), and maps the i-th 
character image on this normalized character image. The normalized 
character image is vertically and horizontally divided into seven sections 
to provide 49 small areas (each having a size of 9 pixels.times.9 pixels). 
The DSP 81 performs raster scanning of respective small areas of the 
normalized image from the upper left to the lower right, and transmits the 
values (0 or 1) of the picture elements of the i-th character image 
corresponding to respective picture elements of the normalized image 
allocated by the above-described mapping to the characteristics extraction 
unit 83. The first characteristics extraction unit 83 incorporates a 
2-line buffer, and stores the data transmitted from the DSP 81 in the 
2-line buffer. If the 2-line buffer is filled up, the 2-line buffer scans 
the data therein using 2.times.2 masks, and calculates the numbers of 
directional indices corresponding to respective patterns shown in FIG. 17. 
The respective directional indices, as shown in FIG. 17, are transferred 
to the DSP 81. The directional index for the k-th small area is 
represented by fkj (j: directional index, j=1, 2, 3 or 4). The DSP 81 
sequentially receives the numbers fkj (k=1 to 49, j=1 to 4) of directional 
indices in all the 49 small areas, and stores them in an internal RAM 87 
within the DSP 81. By performing spatial defocusing processing (as shown 
for example in Japanese Patent Application Public Disclosure (Kokai) No. 
59-177684 (1984)), the DSP 81 converts 196-dimensional vectors fkj into 
64-dimensional characteristic vectors Fmj (m=1 to 16, j=1 to 4), which are 
stored in the internal RAM 87. This processing at step S76 is indicated by 
Bi in FIG. 15. 
Next, two kinds of processing are performed in parallel. The first 
processing is the gross classification processing of the i-th character 
(the next step S77, processing Ci shown in FIG. 15). The second processing 
is the image transfer of the next (the (i+1)-th) character (processing 
Ai+1 shown in FIG. 15), i.e., the process returns to step S74. 
The dictionary 85 for gross classification contains standard patterns for 
respective characters. The standard pattern for each character comprises a 
vector obtained by averaging the characteristic vectors of the character 
for a predetermined number of various types of the character. The 
dictionary 85 for gross classification stores such standard patterns for 
3310 characters. 
At step S77, the distance (the degree of difference) between the obtained 
characteristic vector and the standard pattern (the average vector Mmj) is 
calcuated by means of expression (1) below, and the 48 most significant 
candidate characters having small distances are obtained (gross 
classification processing). 
EQU Distance=.EPSILON.m .EPSILON.j (Fmj-Mmj).sup.2 (1) 
This gross classification processing is performed independently of the DSP 
81, since the distance is calculated using the arithmetic unit 
incorporated within the gross classification unit 84. Accordingly, during 
this processing, the extraction of characteristics of the (i+1)-th 
character image and the detailed classification of the (i-1)-th character 
image (to be subsequently described) can be executed in parallel. 
Next, at step S78, detailed classification is performed. The dictionary 86 
for detailed classification stores previously-obtained average vectors, 
eigenvalues and characteristic vectors for the 3310 characters. The 
eigenvalue and the characteristic vector are the covariance-matrix 
eigenvalue and characteristic vector shown in Japanese Patent Application 
Public Disclosure (Kokai) No. 59-177684 (1984). The distance between the 
above-described characteristic vector and each of the 48 candidate 
characters obtained by the gross classification processing is obtained by 
the pseudo-Bayes identification expression. The candidate characters are 
sorted in the order of smaller distances, and character codes and the 
values of the distances of candidate characters having high accuracy are 
obtained for the 8 most significant (most likely) characters. The results 
of this recognition (the character codes, and the values of the distances 
of the candicate characters) are input to the internal RAM 87 within the 
DSP 81. Such detailed classification is performed by the DSP 81. As can be 
understood from FIG. 15, the above-described detailed classification 
processing is performed in parallel with the gross classification 
processing of the (i+1)-th character image. 
Subsequently, at step S79, the DSP 81 transfers the above-described results 
of recognition stored in the internal RAM 87 to the internal buffer 76. 
When the loop from step S75 to step S79 has been repeated M times, the 
process proceeds to step S80, where the CPU 71 transfers the results of 
recognition for M character images from the internal buffer 76 to the 
communication buffer 73. Subsequently, at step S81, the DMA controller 74 
transmits the results of recognition to the transmission channel via the 
communication control unit 75.