Patent Document

This application is a continuation of U.S. patent application Ser. No.: 11/573,902, filed Jul. 5, 2005, which claims the benefit of priority from Israeli patent application Ser. No. 162921, filed Jul. 8, 2004, the contents of both of which are incorporated herein by reference in their respective entireties. 
    
    
     FIELD OF THE INVENTION 
     The present invention relates generally to a system and method for capturing images and providing automatic characters recognition and processing. More specifically, the present invention relates to an efficient multi-functional universal system and method based on automatic real-time, multi-step identification of characters defining object I.D. 
     BACKGROUND OF THE INVENTION 
     Vehicle traffic and cargo containers transport are undergoing a significant growth rate worldwide (about 10-15% per year). Current security supervision run by both private companies and port authorities is unable to provide a system that will enable efficient traffic control and monitoring in face of the continuing growth. Automation has a major role to play in supporting the efficient handling and capacity required for meeting the growth in container trade. 
     In accordance with the growing numbers of containers entering ports and crossing state borders there is a rising demand for a better surveillance system to monitor incoming and outgoing traffic. The need is voiced in both the private and public sectors. The need is apparent for systems that can efficiently and automatically identify various data such as license plate numbers, personal identification badges, incoming package or mail labels and other various data which appears in number code or other form of alphanumeric characters. Automated check and inspection gateways operation of this magnitude and sophistication is still not utilized in either factories, public companies and organizations or households today. 
     Many traffic environments today already employ various levels of automation, including sophisticated traffic management systems for vehicular traffic and terminal operating systems (TOS) for container movement and inventory management. 
     The automated system described herein include three main Sub-systems:
         1. Image-capturing units, (including illumination devices),   2. A software recognition engine, and   3. System application programs       

     The image-capturing units must include an optical and illumination effective method able to produce images of a container ID number and/or license plate number with sufficient quality, (focus, resolution, contrast, and uniformity), under all operating and ambience conditions, (sunlight, sun glare, night time, adverse weather conditions). The software recognition engine and application programs must be able to process these images and convert them into data for real-time processing. 
     The hardware and software must operate in unison, and be able to read the container and vehicle numbers accurately while they are passing through a gate lane, being lifted or lowered by a container crane, sitting on a chassis slot or handled by other container handling equipment. The design and selection of the Image-capturing and software systems has a great impact on the system infrastructure requirements, determining mounting position and constraints for the cameras and illuminators as well as triggering requirements and solutions. 
     The above applications fail to disclose or teach at least the following:
     1. How to achieve and decipher complete credible identification of data received by the system.   2. These applications approach each application separately and do not describe one whole operative system which will provide an effective solution to the need of such a system in a variety of work places and areas.   3. How to carry out a system self check process without outside interference to assess credibility of data analysis.   4. OCR systems, which these applications are based upon, have further developed and the demand for an expanded system which will answer the need of many and be able to carry out a variety of functions is increasing, these applications do not supply such answer as needed.   

     Thus, there is a demonstrated need for a character recognition system that is capable of providing accurate and precise identification on site, unaffected by outside condition such as weather and visibility, and provides reliable and verifiable results. 
     Furthermore, there is a need for a multi-functional universal system that will provide character identification in a wide variety of fields with the same success. 
     Additionally, the system must be able to perform self-testing and data verification to ensure reliable and repeatable data. 
     The system architecture must be optimized and designed for OCR. Existing recognition systems and method are based on high resolution and/or line scan cameras to capture OCR images, as a result these system generate data that is not always reliable and accurate. In addition these systems are complex, not easy to operate and great energy consumers. 
     The system should be based on components, which are modular with custom-built features for maximum integration, for example lower power consumption, and computer-system control. 
     The system should be able to answer individual needs and demands, and offer information that is both accurate and easily and cheaply accessible. 
     SUMMARY OF THE INVENTION 
     Thus the present invention has the following as its objectives, although the following is not exhaustive. 
     It is an object of the present invention to provide a method and system for identifying alphanumeric codes based on multi-level image processing algorithm. This will enable the most accurate and fast identification of alpha numeric characters based on simultaneous area scans of images. 
     It is a further object of the present invention to provide a method and system for increased reliability that should achieve highly accurate results regardless of weather and lighting conditions and will provide credible identification in any place, at hour of the day year round. 
     It is a further object of the present invention to provide a method and system that will be adjustable and easy to tailor to answer the needs of many business enterprises and provide a solution for a wide range of facilities where fast, reliable identification is necessary. 
     It is a further object of the present invention to provide a method and system that is both highly credible yet easy to operate and does not require expensive hardware and sophisticated computer system to operate under. 
     It is a further object of the present invention to provide a method and system which is easy to maintain and can operate automatically without the need for human supervision and surveillance. 
     It is a further objective of the present invention to provide a method and a system that can read license plate numbers, container I.D marking, chassis I.D marking, aircraft serial numbers etc. After identification and verification the system should be able to automatically operate consequent action such as gate opening or closing, alerting relevant surveillance or security systems and sending identification logs to remote clients. 
     It is a further objective of the present invention to enable operation of this system in all sea or land ports and important border and access destinations without the need for any major changes in the operation and management of these facilities. 
     Yet a further object of the present invention is to provide a system that is easy to assemble and operate, does not require a large space of operation and is comprised of a small number of parts and does not require much energy for operation. 
     Still a further object of the present invention is to provide a system capable of self-check and data verification, capable of error alert if such occur and with the capability of error fixing and removal. The system should be able to create data bases of relative data and present them according to the client specific demands. 
     These objectives and others not mentioned hereinabove are all accomplished by the system and method of the present invention, which comprises a number of sensors, cameras with synchronized illumination systems and a multi-level identification program. 
     The camera and illumination systems are operated whenever an object bearing alphanumeric code requiring identification enters the sensor field. The camera then focuses on the object and images are captured from different angles and zoom positions. Different types of exposures and different illumination spectrums, are employed in order to achieve the best lighting parameters possible. 
     The images can be recorded while the object is on the move, up to object speeds suitable for the intended applications. The images are immediately transferred on to the deciphering program capable of isolating and Identifying the relevant code required. The camera unit continues to capture images as required until the code has been deciphered. 
     The identified number code (number of strings and images) are displayed on the system&#39;s main display, and logged in its local database. Once a code has been recorded it can be used for several applications such as automatic log recording, in conjunction with a security system to limit access or open gates and portals, it can be sent online or using wireless technology to computer terminals or cell phones as necessary. 
     The data stored on the system memory and/or media is useful for maintenance, operation review and installation of different applications such as limited access doorways and gates, providing both identification and log recording of movement within the monitored zone. This type of information management is highly valuable at port terminals and vehicle parking establishments. 
    
    
     
       BRIEF DESCRIPTION OF THE FIGURES 
       The following detailed description of exemplary embodiments of the present invention can best be understood by reference to the accompanying drawings, in which: 
         FIG. 1  is a shows a schematic illustration of a recognition system according to one embodiment of the invention; and 
         FIG. 2 . shows a flow chart detailing the stages of OCRS recognition process in accordance with an exemplary embodiment of the present invention; 
         FIG. 3  depicts a flow chart illustrating the stages of identification algorithm according to some embodiment of the present invention; and 
       In  FIG. 4   a - e  illustrates different recognition code results according to some embodiment of the present invention; and 
         FIG. 5  illustrates an Image capturing unit according to some embodiment of the present invention; and 
         FIG. 6  shows a stand-alone Vehicle Access-Control System according to some embodiment of the present invention; and 
         FIG. 7  illustrate a Vehicle Access-Control System architecture according to some embodiment of the present invention; and 
         FIG. 8  shows a Searching and Processing System according to some embodiment of the present invention; and 
         FIG. 9   a  shows an ISPS vehicle according to some embodiment of the present invention; and 
         FIG. 9   b  shows an exemplary embodiment of single camera ISPS vehicle; and 
         FIG. 10   a , shows an exemplary embodiment of a container code recognition system; and 
         FIG. 10   b  shows a top view of a camera configuration according to some embodiment of the present invention; and 
         FIG. 11 , an exemplary embodiment of a Quay Crane Recognition System; and 
         FIG. 12  shows a screen shot of a multi lane plane recognition system according to some embodiment of the present invention; and 
         FIGS. 13   a - b  show two screen shots of an exemplary embodiment of a Monitor module; and 
         FIG. 14   a , show an exemplary embodiment of a truck and container code recognition system; and 
         FIGS. 14   b  and  14   c  shows a top view of camera configuration according to a truck and container code recognition system. 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
       FIG. 1  illustrates different parts of an optical character recognition system (OCRS)  100 . OCRS  100  combines hardware and software compartments, providing a fast and highly reliable method of capturing and deciphering target images  10 . Target image  10 , defines a recorded image containing the characters OCRS is required to decipher and record. The characters can be any type of alphanumeric code and be placed at different places, such as license plate, chassis body or side of a container. 
     OCRS  100  is comprised of the following Hardware parts: 
     Image capturing unit  110 —including several cameras  112  the number of which varies according to the specific application (i.e. vehicle access-control system and other systems as will be described in  FIGS. 6-14 ) The position and operation of the cameras are also adjustable depending on the specific application. The image capturing unit provides the system with fast and accurate images to process. 
     The ability to perform a highly accurate identification is extended as several images are captured from different angles and with different exposure. Each image is processed separately until complete identification is established. The images undergo adjustment and enhancement to ensure complete reliability, as will be explained in detail in  FIG. 3 . 
     Illumination unit  115 —includes several means of solid-state illumination, required to provide sufficient lighting conditions to enable high quality image capture. The position of illumination subunits, and the level of illumination provided offer a wide range of adjustable lighting conditions adequate for different conditions and requirements. Under this range, target images can be produced in adverse weather and illumination conditions, day or night. 
     The illumination unit  115  is most important as insufficient lighting greatly reduces the chances of achieving reliable code identification. 
     Old fashioned illumination systems are often inadequate, require expensive hardware and consume vast amounts of energy. Solid-state illumination system used in this design was planned to answer specific needs, and is energy conserving as it only operates under program requirements and in correspondence with other OCRS units. The specific operation and integration of the illumination unit will be explained in detail in  FIG. 5 . 
     Video servers units  130 —for capturing camera  112  images and convert them to data. This unit can also record the data on its internal memory, and transmit it as compressed data packets over IP. Video servers units include serial command in/out used for controlling cameras. Some video servers include high transform link such as USB or camera-link. 
     Frame grabbers  132 —receives video signals from Image capturing unit  110  and transmits them, as an array of image pixel bytes to recognition application  160 . 
     The entire process of identification, which usually requires highly sophisticated hardware, can be performed on common PC modules. That is due to the program use of an algorithm that is relatively of low complexity as will be further detailed below. 
     I/0 card  120  main purpose is—1) to deliver sensor  125  alerts, to the processing units 2) to alert the processing unit when an object enters the relevant zone 2) to activate predefined illumination level 3) activate gate (as shown in  FIG. 6 ). 
     Sensor unit  125 —includes a number of sensors placed in different location whose: 
     OCRS  100  employs the following software: 
     Recognition application  160 —an algorithm based program that activates recognition process  200 . Identification process includes: receiving images from the video servers units, deciphering the relevant data, and identify target image  10 . 
     Recognition process  200  is limited to small specific areas within the image and not the entire capture. This fact highly increase the efficiency as well as time and energy saving. recognition application  160  selects areas most likely to contain the target code and recognition process  200  is limited to these areas. 
     Different types of programs exist tailored to meet specific requirements and identify several types of objects. For example programs exist suited to identify vehicle license plates (license identification program), Aircraft tail numbers (plane identification program) etc. a detailed explanation of Recognition application  160  will be described in  FIG. 3 . 
     Management OCRS program (MCP)  170 —for controlling OCRS  100 . MCP  170  manages the OCRS operation and is responsible for saving and transferring the identified codes to client application. 
     MCP  170 —updates management workstation  190  on target object situation  20 . For example management workstation  190  can be port workstation receiving current updates regarding specific crane status. 
     It should be noted that both software and hardware use, as described above, varies in accordance with the demands of each specific identification system (as will be detailed later on in  FIGS. 6-13 ). For example some identification system employ video server units  130  while others employ frame grabbers  132 . 
       FIG. 2 . shows a flow-chart detailing the stages of OCRS  100  recognition process  200 . Recognition process  200  includes 3 phases: Initialization phase  210 , image capture phase  220 , and post image capture phase  230 . 
     Initialization phase  210  includes two steps; 
     In the first step updates  212  are made to OCRS  100 . These updates include data, which might be useful in the process to follow (for example car licenses numbers of authorized cars etc.). The update data is transferred and saved by the recognition program. 
     In the next step, commands  214  are optionally transferred from the video serial link to the relevant cameras  112  to perform focus, and zoom functions on target object  20 , and activate illumination unit  115 . Commands  214  are delivered upon target object  20  entry to detection zone  30 . It should be mentioned that commands  214  step, is activated only by some OCRS  100 , for example TCCRS. 
     Target object  20  is defined as the object, which includes the target image  10 . A vehicle, for example, is a target object containing a license plate which is target image  10 . 
     Detection zone  30 —defined as the area directly in front of the capture zone  40 . 
     Capture zone  40 —defined as the OCRS  100  field of view, within which the camera devices  112  are capable of detecting and capturing the target image  10 . 
     At the end of the initialization phase the system is ready to start the image capture phase  220 . 
     Entry of target object to capture zone  40  triggers software and hardware units, e.g. recognition application  160  and several sensors  125  are correspondingly activated. 
     Each sensor  125  activated, sends a signal via IO card  120  to the recognition application  160 . The signals received alert the system to the object presence within capture zone  40 . 
     Once recognition application  160  receives sensors  125  signals, the image capture phase begins. Different cameras  112  take several shots from different angles and positions and under different illumination levels. The number of images, illumination level, capturing angle, illumination spectrum and exposure levels are all predetermined according to the specific target object  20  requiring identification. The number of images and illumination levels differ, for example, when taking a picture of a car license plate on a clear day or taking a picture at night. 
     Post image capture phase  230  begin once target object  20  has left capture zone  40 , or after a predetermined period set by recognition application  160 . Post image capture phase includes the following steps:
     The images are extracted and sent  232  to recognition application  160  for target code identification.   Once recognition application  160  has deciphered and identified the code within each image OCRS  100  operates recognition process  234  that analyzes the results generated for each image, and compare them. The most accurate result is selected, using identification algorithm (will be described in detailed in  FIG. 4 ) that compare and finalizes the data generated.   At the end of identification process final result=Final Target Code (FTC)  90  is received.   A process of logic verification and validation  236  is generated to verify the reliability of the resulting target code.   

     FTC  90  is saved and presented by OCRS  100  as DDE (Dynamic Data Exchange) message  95 , or alternatively by some other inter-application communication protocol, such as DCOM, or TCP/IP socket service. DDE message  95  is defined as the end result output, which includes information as required by specific client  97 , such as serial number recorded, date and time. Client  97  can save the DDE message or transfer the data to a log file. Additional client application  98  can also use the message information for other purposes. A windows mechanism, “DDE Share”, can also spread the message through network  99  to remote central processors and databases. Other mechanism are also available to speed the result, such as transmitting TCP/IP packets with the recognition results. 
     With reference to  FIG. 3 , a flow chart  300  illustrating the stages of recognition algorithm is shown. Recognition application  160  activates a multi-level recognition algorithm for every target image  10 , separately. Target image  10  is stored in a string type buffer  315  on which the deciphering will be carried out. Apart from buffer  315 , recognition application  160  includes relevant files, stored in picture init file  320  with information and parameters pertaining to recognition process and command instructions that describe the format of target image  10 . 
     Once initial image  325  has been stored and buffered the following occurs:
     1. matrix  330  is constructed, based on target image and several image options according to buffer  315  information and the original file received  320 .   2 searching  335  candidate areas in target image  10 . Candidates areas are areas in target image with greater probability of containing FTC  90 .   3. Selecting  337  the target area out of the potential areas pre-selected before.   4. Adjusting  340  and improving candidate areas which includes separating characters, in candidates areas from the surrounding layers and removing other unnecessary noise elements. This process is carried out by high pass filtering function, and other algorithms.   5. Separating  345  selected characters and examining each, separately.   6. Identifying  350  and verifying each character utilizing various control and standards procedures.   7. transmitting 355 FTC  90  to recognition application database.   

     With reference to  FIG. 4   a - e  results of target code identification are shown. As explained before, the success of the identification requires for several images to be taken with different capture parameters. The logic behind this requirement translates into the following equation: 
     Error of 3 good images=SQRT of error percentage in each of the images captured. For example if 3 good images are captured, each achieving 90% identification success, for each image there&#39;s a 10% chance of error. Thus, the effective error is 1%. 
       FIG. 4   e  depicts the final result (FTC)  90  after activating integration process on all target codes shown in  FIGS. 4   a - d.    
     The integration process includes the following steps:
         i. Comparison of all target code results generated from a certain image with those generating from other images of the same target code  10 . For example if in one image the first character was identified as the number 8 while in others it was identified as the letter B the final result will show B as the first character.   ii. Each character in the target code receives a mark according to the relative accuracy of identification. As In the example given above if the number 8 has a final mark of 40% while the letter B has a final mark of 90% the letter B will be shown in the final code identification results (FTC  90 ).   iii. The integration process also includes comparison of data generated with pre set data from the program database file. If, for example, the first character in the target code was identified as the number 1, and according to the data in the program file the first character is always the letter I, the letter I will be chosen and will be shown in the final code identification results (FTC  90 ).       

       FIG. 5  illustrates an exemplary embodiment of Image capturing unit  110  from the side and from the front. 
     Image capturing unit  110  includes the following parts: 
     camera  112 , illumination unit  115 , camera memory card  113  and illumination card  117 . 
     In the following example the illumination and photography compartments are combined, other identification systems exist in which the two are mounted on separate devices as in the port chassis identification system (TCCRS) where extra strong illumination is required. 
     The type of camera  112  mounting device is determined according to the camera in use. The camera system is designed according to the specific client and location requirements. The illumination memory card  117  features four different illumination levels—low, medium, high and illumination off level. 
     Camera card  117  is connected to illumination card  114 , this fact enables solid-state illumination output to be synchronized to the camera scan exposure time. This method increases the signal to noise ratio, since energy is used only when the exact iris opens. Illumination unit  115  is turned off most of the time, and activated only when the camera unit is activated. This means that the illumination unit is activated only for the duration of fractions of a second depending on the speed of the camera, the energy required for illumination this way is only 15V and 3.3 A. Much energy is saved that way, and lifetime of illumination unit  115  increases and at the same time illumination unit  115  maintenance costs are reduced. In comparison with other systems where a light projector is used and constantly activated during the entire operation, requires 60 A or more energy supply and usually suffers from a short lifetime. 
     During recognition process  234  camera  112  and illumination 115 units operate in the following way: 
     Image capturing unit  110  is incessantly activated 
     When target object  20  enters capture zone  40  recognition application  160  send a command to frame grabber  132  or/and video servers  130  to save images of target object  20 . The command includes all relevant instructions such as the number of pictures required, the degree of illumination and the zoom distance. At the moment of capture, illumination unit  115  provides the necessary illumination level. There are different spectrums used according to target object characteristics, near IR (880 mm) red (660 mm) yellow (590 mm) or other spectrums. 
     The image capture procedure is repeated until a stop command is received. In cases when OCRS  100  includes several cameras, each camera receives an independent command and is operated separately with the appropriate illumination and zoom levels. 
     The different identification systems described here in  FIGS. 6-14  are all based on OCRS  100  as described above in  FIGS. 1-5 , and all include similar hardware and software compartments. These systems mainly differ in system architecture and configuration in regard to the number of cameras and illumination devices, their position and type of sensors and lens being used. Each system is uniquely designed to meet specific objects identification needs. 
     With reference to  FIG. 6 , an exemplary embodiment of a stand-alone Vehicle Access-Control System (VACS)  600  based on OCRS  100  is shown. 
     VACS  600  includes a Compact Car Controller  650  (CCC) installed at the entrance to a secured area or parking lot, gate  620 , and sensor  640 . VACS  600  identifies license plate  610  of a coming car, and automatically opens gate  620  for authorized vehicles  630  without the need for guard. VACS  600  is activated as follows: Sensor  640  (for example a loop detector) indicates the presence of a car  630  and signals CCC  650  to start a new recognition sequence. CCC  650  identifies car  630  and if authorized, opens gate  620 . 
     During Recognition process CCC  650  performs the following steps:
         1. captures multiple images (i.e. car  630  front);   2. locates the license plate  610 ;   3. Identifies the registration number of car  630 ;   4. compares it to an authorized list (which contains registration number (e.g., “ABC123”) and optional owner details (first and last name, e.g., “Bill Smith”)) stored on CCC  650  local database;   5. Opens gate if vehicle  630  is authorized.       

     Users can change the authorized list using cellular phone, Internet, a micro terminal (keyboard and display), or via an external computer. 
     In addition CCC  650  can also transmit recognized registration number through RS232 serial line to a printer, a display system, or as an input to external systems. 
       FIG. 7 , illustrates VACS  600  architecture. Peripherals  710  are connected via a single serial port  715  to CCC  650 . Peripherals  710  comprise: output devices  712 , Micro Terminal  714 , PC host  716  and GSM Terminal (Global System for Mobile Communication)  718 . 
     Micro-Terminal  714  is a small hand-held (or wall mounted) ASCII terminal and keypad, for use with CCC  650 . Micro-Terminal  714  contains a small display and a small keypad. Micro-Terminal  714  is used for management of authorized vehicles. 
     Output devices  712  include various devices connected by user to show information about incoming cars. For example outdoor display (displaying a personalized welcome message) or portable serial printer (printing the events). 
     It is possible to interface the CCC  650  using a PC running Windows (any of the standard operating system), or via network. 
     Member program supports the interface and enables end-user to perform various function such as: “Add”—add a new member to the list, “Edit”—change the car plate number, “Delete”—delete one member from the list, “Find”—find a member by car code first or last name. 
     GSM terminals  718  (i.e. M20 Terminal and SMS (Short Messages Service) of GSM network operator) is used for remote interfacing  722 ,  724  with CCC  718 . 
     CCC  650  comprises: camera  112  and illumination unit  115  (as shown in  FIG. 4 ), OCRS  100  unit (described in  FIGS. 1-2 ). CCC  650  is connected to power supply  760 , sensor  640  (dry-contact indicating vehicle presence) and gate  620 . 
     VACS system uses a solid-state pulsed LED array to illuminate car plate. The illumination is controlled by recognition application  160 , which can set the illumination to 3 levels (after the vehicle triggers the loop detector) or turn it off to save energy (most of the time when there is no vehicle present). 
     The frame grabber settings  132  (i.e. contrast, brightness, gain) are either predefined and constant, selected by a time—dependent look up table, or automatically selected according to the measured brightness of a previous images—implementing an automatic ‘software’ iris. 
     As mentioned above, standard installation is based on the assumptions that reflective car plates are needed to be identified. Thus, near infra-red (IR) illumination is used. For countries (such as Korea and Brazil) where non-reflective car plates are used a visible illumination is activated. Additional special cases include some US states and Mexico where non-standard reflective plates also require a visible illumination. 
     With reference to  FIG. 8 , there is shown an exemplary embodiment of a Image Searching and Processing System (ISPS)  800  that tracks car&#39;s plates, reads and identifies their numbers. 
     The system is mounted permanently on the roof and on the back of ISPS vehicle  850 , which rides along the road and automatically scans parking or passing car plates. 
     The identified number is displayed on the system display  810 , and can be transferred to other Windows applications (via DDE message) or transmitted to Wireless LAN, and personal cell phones  815 . 
     Cameras  112  are mounted to capture one of the following primary situations:
         Parallel parking cars—that are parking in parallel to the road (along the sidewalk or along the road side).   Perpendicular parking cars—cars that are parking on a square angle to the side of the road.   Diagonally parking cars—cars that are parking in an angle to the side of another car.   Passing cars—cars that pass the recognition vehicle on the side.       

     Although the standard recognition vehicle configuration includes dual cameras, there are other recognition vehicle configurations, which include a single camera or triple cameras (two side cameras and one back camera). 
     ISPS  800  constantly toggles, at a high speed rate, between cameras  112  in order to detect the plates from one (or more) of the cameras. The number of cameras determines the number of total views ISPS  800  receives. The chances of achieving accurate identification grows if more images are received, but too many images will slow the operation down and will cause delays in the identification vehicle movement, forcing it to slow down. 
       FIG. 9   a  shows an example of ISPS vehicle  850  with two cameras (front camera  854  and rear camera  852 ) mounted on its roof. Front camera  852  will detect incoming cars (cars  857  and  859 ) which are in field of view  870 , while rear camera  852  will detect plates of outgoing cars (car  851 ) which are in field of view  880 . Thus, each car is scanned twice by front and rear cameras  854  and  852 , which increases the detection capability. 
       FIG. 9   b  Illustrates an ISPS vehicle  850  with a single camera mounted on its back bumper. The identification vehicle has only one field of view that can scan either passing fronts or rears of parking cars. 
     Unlike the systems described above ISPS  800  operates without pause, searching for license plates and reporting finds constantly rather than shutting down between vehicles. 
     With reference to  FIG. 10   a , an exemplary embodiment of a container code recognition system for identifying containers on rail cars TOCRS  900  (train optical container recognition system) is shown. 
     TOCRS  900  tracks and reads Container identification numbers that are carried by a train in a port installation. Each TRCS  900  controls a single rail track, with trains moving at either direction. TRCS  900  detects a single level of containers. Each level includes a single or double container—or no container at all. 
     The identified numbers are displayed on TRCS display, and transferred to other Windows application (with DDE messages), or via the network. The image files could be also saved on disk. 
     When moving train and the containers that it carries enter detection zone, sensors are activated and signal to TRCS program, via IO card, that the container is present. TRCS program starts the recognition process: a sequence of images in different illumination levels are captured according to sensors (as was described in  FIGS. 1-2 ). 
       FIG. 10   b  shows a top view of camera configuration  910  according to TRCS  900 . 
     Cameras  911 ,  913 ,  917  and  919  are located at 4 corners. Side cameras  911  and  919  take images of the side marking of container, while back cameras  913  and  917  take images of the back/front of container. 
     Camera  911  is connected to illumination units  921 , and  923  and camera  919  is connected to illumination unit  927  and  929 . Four sensors  931  are located at the right side of the trail and four sensors  933  are located at the left side of the trail. 
     The cameras, illumination and sensors units are mounted on two horizontal pipes  937 , and  939 . Each horizontal pipe  937 , 939  stand on two vertical poles  941 , which are reinforced to the ground to prevent any effect from passing containers. 
     TRCS  900  comprises the same software and hardware which were mentioned in  FIGS. 1-2 , and it is optimized for identifying railway containers. 
     TRCS  900  operates recognition process  234  as was described in  FIG. 2 . According to TRCS  900  recognition process, target object  20  is defined as container, and the target code  10  is defined as container identification number. 
     The operation of the TRCS  900  is activated according to the following steps:
         1) when moving train and containers that it carries enter detection zone, the sensors are activated;   2) Sensor signal to program via IO card that the container is present;   3) Recognition application starts recognition process  234  which includes the following steps;
           i) a sequence of images in different illumination levels are captured according to the sensors and predefined sequence (the illumination level is controlled by 10 card.   ii) Images are sent to recognition application for container marking identification.   iii) Identification results are sent to recognition application database.   
           4) a single message is generated for each passing container. The message includes recognition results, which contain container ID number, and additional information (such as track/lane number date and time).   5) Message is sent to client application where the message is displayed (Additional client application could also use the message).   6) A windows mechanism (such as DDE Share or DCOM) can also spread the message through the network to remote Central processors and databases (in the case of DDE, DDE client application is provided as source file for simplified integration into other applications.   7) Steps  3 ,  4  are repeated as the train is passing. If the train backs up and forth, additional messages are generated.       

     With reference to  FIG. 11   a , an exemplary embodiment of a Quay Crane Recognition System (QCRS)  970  for identifying containers on quay crane is shown. 
     QCRS  970  tracks and reads Container identification numbers, handled 
     By quay crane. Quay crane  972  is a machine that loads or unloads containers on a ship to/from trucks on pier. QCRS  970  handles various container configuration (20, 40, 45, 20/20 pairs). 
     QCRS  970  comprises PC  190 , installed in crane controller equipment room  974 , an array of 6 cameras  975 - 980  and illumination units mounted on land side and sea side of the crane. 
     As shown in  FIG. 11   b , QCRS  970  reads the images from 2 to 4 different cameras, simultaneously, depending on the type of container that needs to be identified:
     Container  981 —cameras  979  and  976  are used.   Container  983 —cameras  976 ,  980 , 977  and  975  are used.   Container  985 —cameras  980  and  975  are used.   

     QCRS  970  recognition process is similar to that described in FIGS.  1 , 2 . Recognition process  200  is performed once the crate has left the image capture zone (in this case target object defined as container, and target code  10  defined as container I.D). 
     With reference to  FIG. 12 , a screen shot  991  of an exemplary embodiment of a multi lane plane recognition system (PRS)  990 , shows a sample capture of an aircraft. 
     Recognition of identification marking (VH-EBT)  993  is displayed above plane image  994 . History  995  of previous recognitions is displayed below plane image  994 . 
     PRS  991  is designed to identify standard fixed-wing aircraft marking that appear on the vertical tail surface or the sides of the fuselage. PRS  990  sends the recognition results (i.e. plane marking, image file path, lane number, date and time) to client application. 
     PRS  990  includes both hardware and software (as described in  FIGS. 1-5 ) and can accommodate up to six separate lens per system. 
     Apart from the different usage of identification systems as described in  FIG. 6-12 , several monitor modules exist for each unique system design, which serve as support to different OCRS  100  (i.e. VACS, ISPS, TOCRS QCRS and PRS). 
     Monitor module is especially designed to handle multiple applications such as VACS QCRS, connected via the network. Monitor module is usually installed on one or more central servers, or on one of the identification system PCs. 
     The objectives of Monitor utility are:
         1. Monitoring the status of OCRS  100 .   2. summarizing the operation of OCRS  100  graphically.   3. enabling quick access to event log.   4. reporting of OCRS  100  status to external management systems.       

     With reference to  FIG. 13   a , a screen shot  252  of an exemplary embodiment of a Monitor module  250  main display shows status  254  of 14 lanes  256  in a OCRS  100  array. The status  254  of each lane is indicated by different status lights  258  red=error, yellow=warning and green=normal. (In  FIG. 13   a  all 14 lanes are indicate normal status i.e green). 
     Additional information may also be overviewed by monitor modules  250  such as event log  262  for each lane. 
     With reference to  FIG. 13   b , a screen shot  272  of an exemplary embodiment of an Monitor module  250  shows four recognition rate graphs  274 ,  276 ,  278  and  282 . 
     Recognition rate graph  274  describes a summary of a daily recognition percentage for every camera in each lane. 
     Recognition percentage is defined as the number of events with ‘good’ recognition, divided by the total number of recognition events. ‘good’ recognition means that recognition system output any result i.e. a good indication of the quality of camera. 
     As shown in  FIG. 13   b , recognition graph describes lane 6 recognition percentage verses time. 
     Graph  274  shows overall recognition rate (all line 6 cameras) during the entire day (100%). 
     graph  276 —shows recognition rate of back container camera (80-100%). 
     Graph  278 —shows right side camera recognition rate (55-85%). 
     Graph  282 —shows left container camera recognition rate (50-90%). 
     With reference to  FIG. 14   a , an exemplary embodiment of a truck and container code recognition system  150  (TCCRS) is shown. TCCRS  150 , correspondingly and automatically identifies: shipping containers identification number on carrying truck, carrying truck license and wagon/chassis number plate, while the truck and containers are in motion. 
     The identified numbers are displayed on TCCRS display, and transferred to other Windows application (with DDE messages), or via the network (with DDE share networking option). The image files could be also saved on disk. 
     When moving truck and the containers that it carries enter detection zone, sensors are activated and signal to the TCCRS program, via IO card, that the container is present. TCCRS program starts the recognition process: a sequence of images in different illumination levels are captured according to sensors (as was described in  FIGS. 1-2 ). 
       FIGS. 14   b  and  14   c  shows a top view of left and right TCCRS  150  equipment configuration. 
     TCCRS  150  left side shown in  FIG. 14   b  includes: camera  51  mounted on vertical pole  61  and, camera  53  mounted on vertical pole  63 . Connection boxes, which include all needed power supply, are mounted on vertical poles  61 ,  63  and are connected to ground wire conduits. Cameras  55 , mounted on horizontal pipe  65 , are connected to illumination units  57  and  59  and to reflectors  67 ,  69  and  71 . Camera  55  takes images of the side marking of container, while cameras  51  and camera  53  take images of chassis and truck identification number. 
     TCCRS right side, shown in  FIG. 14   c  includes: camera  251  mounted on vertical pole  263  and, camera  253  mounted on vertical pole  265 . Connection boxes  271 ,  273 , are mounted on vertical poles  61 ,  63  and are connected to ground wire conduits. Cameras  279  and  259 , mounted on horizontal pipe  261 , and are connected to illumination units  267  and  269  and to sensors  254 ,  255  and  257 . Cameras  279  and  259  take right images of the side marking of container, while camera  251  and  253  take images of chassis and truck identification number. 
     TCCRS  150  comprises the same software and hardware which were mentioned in  FIGS. 1-2 , and it is optimized for identifying truck containers and truck license and wagon/chassis number plate. 
     TCCRS  150  operates recognition process  234  as was described in  FIG. 2 . According to TCCRS recognition process, target object  20  is defined as container, and the target code  10  is defined as container identification number and truck license and wagon/chassis number plate. 
     Having described the invention with regard to certain specific embodiments thereof, it is to be understood that the description is not meant as a limitation, since further modifications will now suggest themselves to those skilled in the art, and it is intended to cover such modifications as fall within the scope of the appended claims.

Technology Category: 3