Classifications

• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
  • G06V30/00—Character recognition; Recognising digital ink; Document-oriented image-based pattern recognition
  • G06V30/10—Character recognition
  • G06V30/18—Extraction of features or characteristics of the image
  • G06V30/1801—Detecting partial patterns, e.g. edges or contours, or configurations, e.g. loops, corners, strokes or intersections
  • G06V30/18076—Detecting partial patterns, e.g. edges or contours, or configurations, e.g. loops, corners, strokes or intersections by analysing connectivity, e.g. edge linking, connected component analysis or slices
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
  • G06V30/00—Character recognition; Recognising digital ink; Document-oriented image-based pattern recognition
  • G06V30/10—Character recognition

Definitions

• the invention relates to a method for image analysis, in particular for a mobile device with built-in digital camera for automatic optical character recognition (OCR), according to the preamble of patent claim 1 or 2.
• OCR optical character recognition
• Handheld scanners that display, save or transfer scanned text to a computer screen. There are always problems when the original is scanned askew, or only letters of the fragments can be recognized (for example, flag inscribed in the wind). In addition, such techniques fail when direct scanning is not possible (e.g., roadside signage). According to the current state of the art, such an image could be recorded with a high resolution, which can be subsequently scanned. However, there is no direct OCR in the camera itself, as it is too computationally intensive with conventional methods.
• Pattern Matching or, as in the case of handwriting recognition, with the description of the letters by lines and crossing points Pattern matching can be used particularly well in the case of standardized letters (eg In the case of license plate recognition, the characters to be recognized are limited to a small number, which are also standardized.
• DE 10025017 A1 discloses a mobile phone, which in particular for a simpler application and use of additional services and functions such. As short message service, payment transactions, identity or security checks, etc. is suitable.
• the mobile phone has an integrated device for reading characters, symbols codes and / or identity features, which is a scanner, a bar code reader or a fingerprint reader in the form of a CCD sensor, thus providing a comfortable and fast input and capture of text, Symbols or safety-relevant characteristics possible.
• DE 202005018376 U1 discloses a mobile telephone with keyboard, screen, data processing system and an optical scanning system arranged behind an opening or a window of the housing, in particular a hand-held scanner, and an integrated translation program.
• the optical scanning system makes it possible to scan characters and / or words available in another language. Selecting the language translates the word or words.
• the user of the mobile phone is able to read him strange words and texts. This can be beneficial menus, warnings, operating instructions and maps and signs.
• users can also enter words themselves from the keyboard of the mobile phone or select from an encyclopedia contained in the memory of the data processing system. By interconnecting the data processing system with the screen and keyboard, the choice of language translates these words and displays them on the screen.
• DE 10163688 A1 discloses a method and a system for tracking goods which are provided with an optically readable, alphanumeric identification, as well as a detection device therefor.
• the marking is captured as an image by the recording device and converted into image data.
• These are sent by the detection device by radio to a receiver which is connected to a computer system, which further evaluates the image data.
• the image data are evaluated before being sent to the receiver in the detection device. How exactly the evaluation of the image data is done, is not disclosed in detail.
• Object of the present invention is therefore to provide a generic method for image processing in mobile devices with digital camera, which works much more accurate and faster.
• the invention is characterized by the features of independent claim 1 or 2.
• Advantage of the invention is a more robust OCR detection with optional translation in real time (real-time), which also manages with relatively little computing power.
• the robustness refers in particular to the fact that detection works better than conventional systems even under poor conditions (especially light conditions, overlapping interference).
• this is achieved by first performing an adaptive pixel group-optimized preprocessing, which searches the image for lines.
• the most important distinguishing feature of the previously known methods is that now no further direct pattern comparison takes place, but an attempt is made to trace the lines as optimally as possible. From the sequence of movements is then closed on the corresponding character. Because this sequence of movements scale well and describe with relatively little effort This technology is currently suitable for mobile use.
• the sequence of movements of known characters is stored in a search word, so that the movement can be concluded directly on the letter.
• a dictionary / lexicon can be used. If words are recognized by the dictionary / lexicon, the recognized letters can be used for even more optimized character recognition.
• Application scenarios are camera mobile phones for tourists abroad, in particular to read traffic signs, menu cards, general information signs.
• the content can be translated into a second language.
• the translation is shown to the user on the display, or read out via a "text to speech application”.
• the robustness of the recognition is based initially on a normalization of the line widths or letter sizes. Subsequently, the letters are traced, in which case the actual letters are recognized during the tracing.
• the robustness of the detection method results from the combination of different solution steps. Due to the normalization of the bar widths, shadow effects and poor lighting conditions have hardly any influence on the detection rate. Due to the size norms, the
• the image is converted into electrical signals with an image recording element (for example a CCD camera). These signals are then stored according to the method of the patent DE 101 13 880 B4 in a prioritized array.
• a position factor can also be included in the prioritization. The position factor is the greater, the closer the pixel group is to the starting pixel.
• the Startpixel is located in the western languages (English, German, French) first in the upper left corner of the array.
• the pixel groups here can also vary during the recognition process.
• An example of a pixel group is a one-line horizontal array of pixels whose length is dependent on a double change in brightness. For dark letters to be recognized on a light background, the distance between the first light-dark transition and the subsequent dark-light transition would then be a size for an assumed stroke width. Pixel groups of the same assumed bar widths are each compiled in a separate list.
• a low-pass filter In this filter, the sum of n adjacent pixels is taken in each case to find corresponding light-dark, or dark-light transitions. Due to the summation, any pixel errors or errors due to strong noise are greatly reduced.
• each list thus obtained is sorted in such a way that the pixel groups which have a lower Y position are sorted in descending order. If several similar pixel groups are at the same Y positions, new lists are created for them. From these lists, an attempt is now made to derive corresponding vectors. In the process, the pixel groups with the lowest and highest Y values are selected from the respective lists. Between these pixel group positions a line is calculated. Then the deviations of the other pixel groups to this line are determined. If all deviations are below a certain threshold, then a description vector has been found for this list.
• the list is split and an attempt is made to generate corresponding vectors for each sub-list. It makes sense to divide the list where the largest deviations from the calculated line occurred. In this way one obtains a number of vectors. Touching vectors are summarized in another vector list and sorted according to the Y values. This vector list then describes corresponding letters. The vector list is then normalized (eg to the maximum Y difference). Such a normalized vector list can then go through a solution tree in which the different letters are stored. With this approach you will first recognize only a part of the letters. However, you get in this way first information about the font to be recognized. For large characters you will get double letters.
• recognition errors with dictionaries could be partially detected and corrected.
• the output of the recognized characters can be realized both via a display and via a "speech-to-text program".
• the method described describes an optimized method which forms vectors from pixel-based images, wherein each individual pixel (with a one-line pixel group) only needs to be traversed once.
• an edge optimization is usually carried out to increase the recognition rate beforehand, and only then is the recognition process started. In the method described above, this is done in one step, so it is both less computationally intensive and more robust.

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• Engineering & Computer Science (AREA)
• Computer Vision & Pattern Recognition (AREA)
• Physics & Mathematics (AREA)
• General Physics & Mathematics (AREA)
• Multimedia (AREA)
• Theoretical Computer Science (AREA)
• Character Discrimination (AREA)
• Character Input (AREA)

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• 2007
  • 2007-11-05 DE DE102007052622A patent/DE102007052622A1/de not_active Withdrawn
• 2008
  • 2008-10-28 RU RU2010122947/08A patent/RU2454718C2/ru active
  • 2008-10-28 CA CA2704830A patent/CA2704830C/en active Active
  • 2008-10-28 EP EP08848083A patent/EP2208170A1/de not_active Ceased
  • 2008-10-28 BR BRPI0820570-1A patent/BRPI0820570A2/pt not_active Application Discontinuation
  • 2008-10-28 MX MX2010004732A patent/MX2010004732A/es active IP Right Grant
  • 2008-10-28 KR KR1020107012446A patent/KR101606469B1/ko active IP Right Grant
  • 2008-10-28 CN CN200880114728XA patent/CN101855640B/zh active Active
  • 2008-10-28 US US12/740,047 patent/US8532389B2/en active Active
  • 2008-10-28 WO PCT/EP2008/009093 patent/WO2009059715A1/de active Application Filing

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