GB2403007A - Estimating text color and segmentation of images - Google Patents

Abstract

In some embodiments, the invention includes receiving a digital image including text and background. The method includes vector quantizing the digital image such that the digital image is divided into certain colours, and creating a text colour histogram from a portion of the text and a first portion of the background. The method also includes creating at least one background colour histogram from a second portion of the background, and creating a difference colour histogram from a difference between the text colour histogram and the at least one background colour histogram, and wherein an estimated colour of the text is derived from the difference colour histogram. In other embodiments, the invention includes receiving a text object including bounding boxes of multiple frames of a video signal. The method further includes estimating a colour of text of the bounding boxes and aligning blocks representing the bounding boxes through a best displacement search in which only pixels having a colour within a threshold of an estimated colour are considered. Some embodiments of the invention also include receiving digital images in text bounding boxes and in preparation for a segmentation process, adjusting sizes of the digital images to a fixed height.

Description

ESTIMATING TEXT COLOR AND SEGMENTATION OF IMAGES

Background of the Invention

Technical Field of the Invention The present invention relates generally to localization and/or segmentation of text in images.

Background Art.

Existing work on text recognition has focused primarily on optical recognition of characters (called optical character recognition (OCR)) in printed and handwritten documents In answer to the great demand and market for document readers for office automation systems. These systems have attained a high degree of maturity. Further text recognition work can be found in industrial applications, most of which focus on a very narrow application field An example is the automatic recognition of car license plates Proposals have been made regarding text detection in and text extraction from complex images and video. However, as can be seen from reading their descriptions, they are each non-general in some aspect Further, some do not involve removal of the localized text from

its background.

Accordingly, a need for a generalized approach of text localization and segmentation remalos.

Summary of the Invention

According to a first aspect of the invention, there Is provided a method comprising receiving a text object including bounding boxes of multiple frames of a video signal; estimating a color of text of the bounding boxes, and aligning blocks representing the bounding boxes through a best displacement search in which only pixels having a color within a threshold of an estimated color are considered According to a second aspect of the Invention, there is provided n apparatus comprising a machine readable medium having Instructions thereon which when executed cause a processor to perform a method including receiving a text object including bounding boxes of multiple frames of a video signal, estimating a color of text of the bounding boxes, aligning blocks representing the bounding boxes through a best displacement search In which only pixels having an estimated color are considered According to a third aspect of the Invention, there is provided a method composing.

receiving digital Images In text bounding boxes, and in preparation for a segmentation process, adjusting sizes of the digital images to a fixed height, wherein if a particular one of the digital Images has a height smaller than the fixed height the dgtaMmage Is Increased In height and If the particular one of the digital Images has a height greater than the fixed height, the digital Images Is reduced In height According to a fourth aspect of the invention, there Is provided an apparatus composing a machine readable medium having Instructions thereon which when executed cause a processor to perform a method including receiving digital Images in text bounding boxes, and In preparation for a segmentation process, adjusting sizes of the digital images to a fixed height, wherein if a particular one of the digital images has a height smaller than the fixed height the digital Image Is Increased in height and If the particular one of the digital images has a height greater than the fixed height, the digital Images Is reduced In height

Brief Description of the Drawings

The Invention will be understood more fully from the detailed description given below and from the accompanying drawings of embodiments of the Invention which, however, should not be taken to limit the Invention to the specific embodiments described, but are for explanation and understanding only.

FIG 1 is a flow diagram representing various functions performed in some embodiments of the Invention FIG 2 is flow diagram representing images at various stages of localization In some embodiments of the invention FIG 3 Illustrates examples of Initial bounding boxes for an Image In a frame having text

and a background.

FIG 4 illustrates examples of vertical and horizontal projection profiling.

FIG. illustrates vertical segmentation applied to a portion ofthe text of FIG. 3 FIG. 6 illustrates horizontal segmentation applied to a portion of the text of FIG. 3.

FIG. illustrates an image on a web site that includes text and a background.

s FIG. is partially block diagram, partially flow diagram representation of color estimation through quantization according to some embodiments of the invention.

PIG. 9 is a flow diagram representing a relation between video monitoring and text tracking according to some embodiments ofthe invention.

FIG. 10 is a block diagram representation of a computer system that can perform JO functions according to some embodiments of the invention.

Detailed Description

1. Introduction

Various embodiments ofthe present invention involve localization and/or segmentation oftext in images, wherein the images may be still or motion images, such as in video or Web pages. Web pages may include video or nonvideo images. The text is not required to be in a particular location in the image or have a particular color. Further, the background (also called non-text) may have a simple (e.g., monochrome) or complex

background.

Efficient indexing and retrieval of digits! video is an important aspect of multimedia databases. 1 he text in videos is one powerful high-leyel index for retrieval.

Detecting, extracting and recognizing text can build such an index. It enables a user to submit sophisticated queries such as a listing of all movies featuring John Wayne or produced by Steven Spielberg. Or it can be used to jump to news stories about a specific 2s topic; since captions in newscasts often provide a condensation ofthe underlying news story. For example, one can search for the term "Financial News" to get the financial news ofthe day. The index En also be used to record the broadcast time and date of commercials, helping the people who check for their clients whether their commercials have been broadcast at the arranged time on the arranged television channel. Many other so usefijl high-level applications are imaginable if text can be recognized automatically and reliably in digital video. Segmenting and recognizing text in the non-text parts of web pages is also an important issue. More and more web pages present text in images.

Existing text segmentation and text recognition algorithms cannot exact the text. Thus, all existing search engines cannot index the content of image-rich web pages properly.

Text segmentation and text recognition might also help in automatic conversion of web pages designed for large monitors to small LCD displays of appliances, since the textual content in images can be retrieved.

2. Overview FIG. 1 provides a flow diagram which is useful to give an over view relative to some embodiments ofthe invention. FIG. I includes a text localization block I O and a 0 text segmentation block 14. Reference in the specification to "an embodiment," "one embodiment," "some embodiments," or "other embodiments" means that a particular feature, structure; or characteristic described in connection with the embodiments is included in at least some embodiments, but not necessarily all embodiments, of the invention. The various appearances "an embodiment," "one embodiment," or "some embodiments" are not necessarily all referring to the same embodiments.

2.1 Text Localization: A digital input signal (which typically includes an image) is received by feature extraction block 20 of text localization block 10. In some embodiments, any ASCII or related text (e.g., Hit=, text) is removed before or as the input signal is received by feature extraction block 20. Note that a Web page could have multiple images that are treated as separate images. The text localization block funds locations of text in images and marks them by tight text bounding boxes. In some embodiments, these bounding boxes should circumscribe only one text line of one text column. However, as described below, a text column is not limited to a single character.

In some embodiments, localization includes the following: (1) Feature extraction (block 20): A feature is extracted from the input image, which capture features unique to text.

(2) Region classification (block 22): Each pixel In the feature image is classified whether it belongs to text or not. Based on this information initial text bounding boxes are created.

(3) Region consolidation (block 24): The text bounding boxes are refined such that each contains only one line and column oftext.

(4) Text Tracking (block 26): If the input is video, this block is added to the processing. Here we exploit the temporal redundancy of video to improve the precision of text bounding boxes as well as to remove many false alarcns.

(5) Estimating text and dominant background color (block 28).

2.2 Text Se,,mentation The text segmentation stage (block 14) removes the background (non-text pixels) and creates an output signal. The output signal on output 44 is an image text representation. An example of an image text representation is a text bitmap. The text bitmap may include, for example, a black text on a white bacl;ground, regardless ofthe colors ofthe original text and background. The bitmap may be used by lo text recognition software to identify the particular text that has been localized and segmented by blocks 10 and 14. As an example; the text recognition software may be standard QCR sofl.,ae, which expects black text on a white background, although the invention is not limited to producing such an output signal.

To improve segmentation, each text box is scaled to a height of, for example, 100 is pixels (blocl; 30). Next, the background is removed (blocks 32 and 36). The search for background pixels starts on the border ofthe text bounding box. For video, this may be preceded by sub-pixel accurate aligarnent ofthe bitmaps ofthe same text (block 34). The remaining pixels may be binarized (block 38). As mentioned, the resulting binary bitmaps can be fed into standard OCR software to transcribe their content into, for example, ASCII.

The invention is not restricted to the particular blocks (10 and 14) of FIG. 1. In different embodiments, the details ofthese blocks (20 - 38) may be different and some blocks could be eliminated, consolidated, or have a different order.

3. Addition_I overview formation and surnma Some embodiments ofthe text localization and segmentation system belong to a class of top-down approaches. Potential text lines are refined in case of video by exploiting I'd temporal redundancy (section S). Like in the text localization, the text segmentation may also use the temporal redundancy of video to improve the segmentation result. Several basic decisions are involved in some embodiments. They include: so (I) Only horizontal text is considered since this is true for more than 99% of all artificial text occurrences. Experiences with older systems, which considered any writing direction, suggests that the missing 1% oftext occurrences would be paid off by a much higher false alarm rate. As long as a performance of>90% correctly segmented text in videos and images is still a challenge, nonhorizontal text can be neglected.

(2) Non-text regions are much more likely than text regions. Therefore, we decided to train the raw text detector as tight as possible (trained for a specific size of text at a specific position). Scale and position independence may be achieved by a applying our text detector at all positions in all scales.

Another decision is that text occurrences only matter if they consist of a least two letters or digits.

0 The invention is, however, not restricted to the particular details mentioned above.

For a particular applications, it may be known that vertical text will be used, in which case, it can be accommodated. Further, if other information is known about the image, the particular embodiment of the invention may be modified to take advantage of that knowledge.

4. Text Localization Referring to FIG. 2, an image 50 is scaled into multiple image 52, 54, 56, 58, and of different sizes. The images may be still images or image frames in video. Although five images are shown, the number may be more or less than five. The edge orientation of pixels in the image is determined to create feature images 62, 64, 66, 68 and 70 (see section 4.1). A fixed scale text adapter is applied to classify pixels in the edge orientation image to create images 72, 74, 76, 78, and 80 (see section 4.2). Images 72-80 are integrated into one saliency map 84 associated with an image (see section 4.3). Initial text bounding boxes are created from saliency map 84 (see section 4.4.1). The text bounding boxes and an associated image which is the same as or similar to image 50 are represented by block 86. The text bounding boxes of block 86 are revised (e.g., consolidated) (see section 4.4.2) to created revised text bounding boxes as represented by block 88 which also represents the image associated with block 86. Note that the text bounding boxes are not part of the image, but are associated with the image.

4.1 Innate Features Artificial text occurrences have been commonly characterized in the research community as regions of higl1 contrast and high frequencies. There are many different ways to amplif y these features. One way is to use the gradient image of the ROB (red, green, blue) input image l(x,y) = ,'Ir(x,y), Tg(x,y), Ib(x,y)) in order to calculate the cornplex values edge orientation image E. E is defined as follows: Let Ac(r,cp) be the angular coordinate representation of the Cartesian derivation image VIc(x,y) of color plane c.

Then, E is defined as the Cartesian coordinate representation of A(r,;p mod I 80 ) Ac(r,mod 180). The module 180 degree is applied to convert direction into CG(r,gib) orientation. E serves as our feature for text localization.

0 Another way is to use the directional derivation DCx and DCy of image band b to calculate the directional edge strength E,:= [DCxlandEy= iDC I ' CG(rtg,6} C(',g,6} as well as its overall edge strength E = 1/3 ((DCx)2 (Dc))in ce(r.1 lS 4.2 Fixed scale text d_ector In some embodiments, a fixed scale text detector is used to classify each pixel in the edge orientation image E based on its local neighborhood whether it is part of a text region of certain size. For example, given a 20x10 pixel region in an edge orientation image E, the fixed scale text detector classifies whether the region contains text of a So certain size. There are many different techniques for developing a classifier. Examples include a Bayes classifier, a mixed-gaussian classifier, and a neural feed-forward network (which has a good generalization capability. For our work we compared the performance of a Bayes classifier using the Neyrnan-Pearson criterion with the performance of a real valued and complex-valued neural feed- forward network. The complex-valued neural network with a hyperbolic tangent activation function may provide superior performance.

In some experiments, at a comparable hit rate (90%), its false hits (0. 07%) on the validation set was more than twice as low than with a comparable real-valued network.

KeQre. Various network architecture may be used. some embodiments, 200 complex-valued neurons fed by a 20x10 edge orientation region in E serve as nehvork input. This size ofthe receptive field exhibits a good tradeoffbetween performance and computational complexity. An input layer of 30x15 neurons achieved s not better classification results, but was computational more expensive. On the other side, using an input layer with less than 10 rows resulted in substantially worse results. Note that the number of rows ofthe receptive field determines the size ofthe font being detected since all training text patterns are scaled such that the fonts size is equal to the number of rows. The input layer in tuft is connected to a hidden layer of 2 complex-valued neurons.

0 Again, using more hidden neurons did not result in any performance improvements, while using on ly one increased the false alarm rate by a factor of three. The hidden layer is aggregated into one real-valued output neuron.

Network Train no. There are various ways to accomplish network training. The following describes some ways, but the invention is not so limited. The training and IS validation test set should be as small as possible while skill being representative. It should contain all typical text patterns and non-text patterns. Theoretical investigations have shown that neural networks will be most efficient, if the relation between the number of text and non text samples in the training set corresponds to the relation of the two groups in the application. A quantity of training samples which fulfill this criteria is obtained.

so While it is straihtfo.-ward how to get examples for different types oftext, it may be more difficult to get a representative non-text set.

A solution to this problem is the so-called "bootstrap" method. The composition of the training set may seriously affect a network's performance. In some embodiments, a representative set of 3 0180 text patterns rmd 140436 non-text patterns were collected.

Initially 6000 text patterns and 5000 non-text pattern were selected randomly for training.

On 1y the non-text pattern set was allowed to grow by another 3000 patterns collected by means of the "bootstrap' method. This method starts with an initial set of non-text patterns to train the neural network. Then, the trained network is evaluated using a validation set distinct from the training set (here: all patterns minus the training set). Some of the falsely classified patterns of the validation set are randomly added to the training set and a new, hopefully enhanced neural network is trained with this extended and improved training set. The resulting network is evaluated with the validation set again and further falsely classified non-text patterns are added to the training set. This cycle oftraining and directed adding new patteMs is repeated until the number of falsely classified patterns in the validation set does not decrease anymore or - like in our case - 3000 non-text patterns s (and only nontext patterns) - have been added. This iterative training process guarantees a diverse training pattern set.

Given a properly trained neural network, a 20xl 0 pixel window slides over the edge orientation image E and is evaluated at each position. The network's response is stored in a so-called response image by filling the associated 20xlO region in the response lo image with the networks output value if and only if it exceeds the = 0 (between -1 and 1). Since a step size of one may be computationally prohibitive for large images or high definition television (ADDS) video sequences, we use a step factor of 3 and 2 in the x and y direction, respectively. It may be that the subsampling does not causes any decrease in accuracy but a speed- up of 6x.

Under other embodiments, using a real valued network, logistic activation function, at each window location, the output of the neural network is tested if it exceeds thnhVo,k 0.85 (between 0 and 1.0). If so, a box of 20 X 10 filled by the neural network's output value may be added to the associated position in the response image.

4.3 Scale Integration.

so In some embodiments, the raw fxed-scale text detection results at all scales (images 72 - 80) are integrated into one saliency map of text in order to recover initial text bounding boxes. (See, FIG. 2, block 82.) In many situations, text locations identify themselves as correct hits at multiple scales, while false alarms appear less consistent over multiple scales. A saliency map may be created by projection of the confidence of being Is text back to the original scale of the image. (An example of the confidence of being text is an activation level of the neural network output.) The saliency map may be initialized by zero. Then, for each detected bounding box at each scale its confidence value of being text is added to the saliency map over the size of the bounding box at the original image scale There may be more than one bounding box in a given scale within the vicinity of a so particular area. In some embodiments, the saliency map may reflect the total number of bounding boxes from all image sees - -thn--c vicinitof=-pariieular area. q

4.4 Extraction of Text Bounding Boxes 4.4. 1 o There are various ways to create text bounding boxes. The following describes techniques for some embodiments, but the invention is not restricted to these details. To create an initial set of text bounding boxes around regions of strong saliency, the algorithm starts to search for the next not yet processed pixel in the saliency map with a value larger then a pre-specified threshold thCorC. The choice of the threshold's value is determined by the goal to avoid the creation oftext boxes for non-text regions. Non- text regions should to be less salient. For our classifier, Theo, = 5.0 worl;ed fine, however, it may have to be adjusted (e.g., if a new neural network is trained). A number other than 5.0 could be used.

Once a pixel, called core pixel, in the saliency map with value P(x,y) > theory is found, it is taken as a seed for a new text box of height and width l. This new text box is then expanded iteratively. The following pseudo code (called Pseudocode Example 1) gives an ' example of the Initial text box creation algorithm.

Initial text box creation algorithm (Pseudocode Example 1): (1) search for next core pixel and create a new text box of width and height I. (2) do (3) extendNorth(box) (4) extendEast(box) (a) extendSouthbox) (6) extendWest(box) (7) while (box changed) The average intensity ofthe pixels ofthe adjacent row above the total width ofthe box in the overall edge strength image is taken as the criterion for growing in that direction. [fthe average intensity is larger than th,gjon - 4.5, the row is added to the box.

This value is chosen to be a little bit smaller than throb in order not only to get a text box including the core of a text region, but a text box that encompasses all parts of the text.

Next, the same criterion is used to expand the box to the left, bottom, and right. This iterative box expansion repeats as long as the bounding box keeps growing (see Pseudocode Example 1).

FIG. 3 illustrates date and time in an image in a video frame I I O and examples of initial bounding boxes, although the invention is not restricted to the particular examples.

The bacl;ground of frame 1 10 could be a solid color (such as white as illustrated) or a more complex background with different colors of various shapes. The text bounding s boxes are illustrated as dashed lines. There could be additional text in image 110.

4.4 2 Revised_Text Bounding Boxes Glee initial bounding boxes omen do not optimally frame the text in the image: In practice, some boxes contain no text (false alarms); others span more than one line and/or column of text, and in many the background make up a large portion of the pixels.

to Fortunately, these shortcomings can be overcome by an iterative postprocessing procedure utilizing the information contained in so-called projection profiles.

A projection profile of an image region is a compact representation of the spatial pixel content distribution and has been successfully employed in document text segmentation. While histograms only capture the frequency distribution of some image feature such as the pixel intensity (all spatial information is lost), intensity projection profiles preserve the rough spatial distribution at the cost of an even higher aggregation of the pixel content. A horizontal/vertical projection profile may be defined as the vector of the sums of the pixel intensities over each column/row.

FIG. 4 shows an example in which vertical and horizontal projection profiles are depicted as bar charts along the x and y axes ofthe feature images. The upper boundaries of the text lines are marked by steep rises in the vertical projection profile while the lower boundaries are marked by steep falls. Similarly, the right and left boundaries of text objects are indicated by steep rises and falls in the horizontal projection profile. These steep rises and falls can be identified as locations where the profile graph crosses an as adaptively set threshold line. Down-up transitions are signified by a long line and up down transitions are signified by a short line (as labeled in FIG. 4.

Ike term "text object" is used as follows. In the case of a single image, a text object is a text boundary box (including one that has been through the revision process).

the case of video, a text object includes multiple text bounding boxes (including those that have been through the revision process) from different frames in time. Stated differently, in the case of video, the text object includes different instances of the same text Tom different frames (images).

An example of a vertical sernentation algorithm is given in pseudocode form in Pseudocode Example 2. An example of a horizontal segmentation algorithm is given in s pseudocode form in Pseudocode Example 3. However, the invention is not limited to the particular details shown in Pseudocode Examples 3 and 4. There are other ways to implement embodiments of the inventions. Note that the term "segmentation" is used in this section in connection with revising initial bounding boxes and in section 6 to refer

generally to removing text Com the background.

Vertical segmentation algorithm (Pseudocode Example 2): hi) expand box attire top and bottom bythe minimum of haifthe height ofthe original text box and half the possible maximal text height (2) calculate vertical projection profile ofthe IE Is (3) get minimum and maximum profile values (4) calculate the segmentation threshold (a) set changed false (6) for all rows ofthe profile (7) if (profile[current row] > threshold) (8) if (no upper boundary yet) (9) set upper boundary = current row (I O) else (11) if (no lower boundary yet) ( 12) set lower boundary = current row (13) if (upper boundary) (14) create new box using the values of the upper and lower boundaries (15) unset current upper and lower boundaries (16) set change = true (17) delete processed box Horizontal segmentation algorithm (Pseudocode Example 3): (l) expand box at the left and right by the minimum of halfthe height of the original text box and halfthe possible maximal text height (2) articulate horizontal projection profile ofthe [E! (3) get minimum and maximum profile values (4) calculate the segmentation threshold (hi) for all columns of the profile (6) if (profile[current column] > threshold) (7) if (no left boundary yet) 4 (8) set left boundary current column (9) else if(right boundary) ]2 (Jo) if (gap between current column and right boundary is large enough) (11) create new box from Jeff and right boundaries (12) unset left and right boundaries (13) else (14) unset right boundary (15) else if (no right boundary) (16) set right boundary = current column (17) if(left && no right boundary) (IS) right boundary = last column lo (19) if(left and right boundaries) (20) update processed box to current rightlleft boundaries (21) else (22) delete processed box With reference to Pseudocode Example 2, in some embodiments, the vertical se;l'entation algorithm applied to each text box works as follows, although the invention is not limited to these details. The box is enlarged at the top and bottom (lines (1) and (2) in Pseudocode Examples 2). The enlargement is desirable because the correct boundary So may lie outside the current box and therefore the initial boundaries accidentally may cut offs portion of the text. To recover these boundaries correctly, some rows outside the original box should be taken into consideration. We set the top and bottom enlargements to the minimum of half the height of the original text box and half the possible maximal text height. While half the height of the original text box seems to be a good worst case as estimate for imperfection in the initial vertical boundaries, the restriction to halfthe maximal possible text height is used because the original text box could contain more than one line oftext and therefore half the height of the text box might be larger than the maximal possible text height.

Next, the vertical projection profile over the enlarged box of the feature image IEI is JO calculated as svell as the maximum and minimum values maxi and mint in the profile. To determine whether a single value in the projection profile belongs to a text line, a threshold thresh may be c alculated as threshing = minp,Onje + (maxp,fie minprOfile) x 0. 175. (Note line (4) in Pseudocode Example 2). The factor of 0.175 was chosen experimentally and may be different in other embodiments. Every line with a vertical profile value exceeding threshes' is classified as containing text.

In lines (6)-(8) of Pseudocode Example 3, the algorithm begins to search for the first down-up transition Starting from the top. This row is marked as a potential upper bound of a text box (line 9). Then, the next up-down transition is searched in the projection profile (line 13). If found a new box with the current upper and lower s boundaries is created. The search for a new pan' of down-up and up-down transitions continues until all elements in the projection profile have been processed. Finally, the original text box may be deleted. The text box is now split into its text line. See FIG. 5, which shows vertical segmentation applied to a portion of the frame of FIG. 3. Note that additional revisions should be performed to the bounding boxes shown in FIG. 5.

0 Analogously, the horizontal segmentation algorithm (Pseudocode Example 3) is applied to ensure that text in one line which does not belong together is separated.

However, in some embodiments, two differences may exist between Pseudocode

Examples 2 and 3:

(1) A factor of 0.25 instead of 0.175 is used in the computation of threshed.

Experimentally, this value has proven to be superior for the horizontal segmentation.

(2) A gap parameter has been added. Unlike the vertical segmentation words in the "same" column should not be split up due to small gaps between the individual words. Therefore, the gap parameter is needed to bridge these low horizontal profile values if necessary. If the algorithm has found already a pair of down-up and up-down transitions and thus a pair of potential left and right boundaries and if the gap between the found up-down transition and the current column is large enough, the down- up transition found on the current column is interpreted as the left boundary of a new text object and a new box is created from the formerly found pair of transitions. The current column is marked as a new potential left boundary. If the gap is not large enough, the algorithm interprets the valley in the profile as being to small and consequently ignores (deletes the potential left boundary found so far). The algorithm continues with the next value in the profile. The invention is not limited to these details.

FIG. 6 gives an example ofthe result ofthe horizontal segmentation algorithm.

Note that additional revisions should be performed to the bounding boxes for more complex layouts.

}FIG. 7 illustrates text "DOW JONES Cornrnodities trading is risking and is not for everyone'' in an image 120 that includes a background 124. Image 120 is in a webpage 126. Background 124 may be a single color or a complex background (e.g., with many colors of different.shapes). The vertical segmentation algorithm may not initially separate the different text lines of "Commodities trading involves risk and is not for everyone." The reason for this becomes clear if one imagines what the vertical projection profile for the respective text box looks like. The text box in the left column may mask the vertical profiles of the smaller text to the right which therefore could not be split into two text lines. On the other hand, tee gap between these two text columns is large enough to be 0 split up after the horizontal segmentation algorithm was applied. Experimentally it turns out, that almost every layout can be divided into its text rows and columns if a few cycles (or passes) of vertical and horizontal segmentations are applied to the text boxes.

Since the text height in images as well as in video frames is limited, in some embodiments, boxes with height < mini = apt or height > maXcxthcight = imagehCichr/9 are classified as non-text regions and therefore discarded. Moreover, since horizontal segmentation assures that text boxes contain text objects like words or text lines, the height of correctly segmented text boxes should be smaller than their width.

Consequently, boxes with height > width may be discarded, too. Finally, text boxes which have the same upper and lower boundary and are close enough to touch or overlap each other may be joined into one text box. This 2s reduces complexity and may later enable a more stable text tracking throughout time.

4.4.3 Estimating Text Color and Background Color

some embodiments, estimates ofthe text color and background color for each text bounding box are made The estimates may be used to detennine whether a text bounding box contains nor; text (dark text on bright background) or inverse text (bright text on dark background). {rnages are typically colorful. Even a visually single-colored region lilac a character in a video frame consists of pixels of many different but similar colors. Accordingly, the complexity Dfthe color distribution in each text bounding box may be reduced by quantizing the colors to, for example, the four most dominating colors.

A variety of vector quantizers may be used. In our work, we used a fast vector quantizes, which are readily available.

A text color histogram provides a measure ofthe amount of the quantized colors included text in a bounding box. The measure may be of a sample of the text, for example, the four center rows of the text bounding box. The colors measured by the text color histo,grarn would typically also include some background intermingled between letters and inside some letters (e.g., "o"). Of course, portions of the text other than the four center to rows could be used for the text color histogram.

A background color histogram may provide a measure of the amount of the quantized colors included in portions of the background. For example, the portions could be two rows directly above and below the text box (four rows together). Note that this background color histop,rarn can include components from two background color is histograms (e.g., one from above the text and the other from below the text).

Alternatively, there might be only a background color histrogram from above the text or one color histogram from below the text.

In some embodiments, a difference histogram is calculated between the text and background histograms. The maximum color ofthe difference histogram is very likely to correspond to the text color and the minimum color ofthe difference histogram to the dominating background color. This methodology was proved experimentally to be very reliable for homogeneously colored text. Of course, it may fail for multi-colored text, which is rare.

Based on the estimated text color and the most dominant background color we z5 estimate whether a text bounding box contains normal tent or inverse text, described above. If the grayscale value of the text color is lower than dominant background, we assume normal text, otherwise inverse text.

FIG. 8 is a block diagram illustration ofthe use of vector quantization and the use of color histograms to estimate the color according to some embodiments of the invention.

Other embodiments have different details. Referring to FIG. 8, block 130 represents a bounded text box and surrounding background before it is vector quantized (VQ). Block l 134 represents the bounded vector quantized text signal and background. After VQ, the text signal including background has only four colors. A text color histogram CHT is created from, for example, a strip (e.g., four center rows) through the center oftext. Upper and low color histograms CHu and CHL are created from a strip (e.g., two rows) above the s text and from a strip (e.g., two rows) below the text, respectively. In the example, 4 colors are allowed. Therefore, the color histograms provide a representation ofthe amount of each of the colors C 1, C2, C3, and Cal included in the strips after VQ. A difference color histogram CAD is created, where Coy -- CAT- CHU CHL. As suggested above, color histograms CHU and CAL may be summed before being subtracted from CHT.

in Note that the estimated color may be used as described in sections 6.2. 2 and 6.3, below. However, in section 5 to section 6.2. i and the first part of section 6.2.2, images with gray scale colors (such as the image 88 in FIG. 2) may be used.

5. Exploiting Information Redundancy in Videos Video is distinguished from still images and non-video web pages by temporal redundancy. Typically, each text line appears over several contiguous frames. This temporal redundancy can be exploited to: (1) increase the chance of localizing tent since the same text may appear under varying conditions from frame to frame, (2) remove false text alarms in individual frames since alley are usually not stable throughout tune, (3) interpolate the locations of"accidentally" missed text lines in individual frames, and (4) enhance text segmentation by bitmap integration over time.

However, exploiting this redundancy may be computational expensive, and 2s applying our text localization scheme of section 4 may be prohibitive. To see this, suppose the image-based text iocaiizer needs about 5 seconds per MPEG-I video frame.

Processing a minute of video could add up to 2.5 hours! MPEG refers to Moving Picture Experts Group. Current and proposed MPEG orrnats include MPEG-I (r'Coding of Moving Pictures and Associated Audio for Digital Storage Media at up to about 1.5 MBit/s," ISO/IEC JTC I CD IS-l 1172 (1992)), MPEG-2 ("Generic Coding of Moving Pictures and Associated Audio, " ISO/IEC JTC 1 CD 13818 (1994); and MPEG=4 ("Very Low Bitrate AudioVisual Coding" Status: call for Proposals I 1.94, Working Draft in 11.96). There are different versions of MPEG-I and MPEG-2. Various formats other than MPEG may be used.

s S. 1 Text Obiects In the case of still images, all localized text bounding boxes are generally independent and unrelated to each other. To exploit the redundancy inherent in video, text bounding boxes of the same content in contiguous Earn es may be summarized into one text object based on the visual contents of the text bounding boxes. In the case of video, a 0 text object describes a text line over time by its image text representation (e.g., bitmaps), sizes and positions in the various frames as well as its temporal range of occurrence.

Complete text objects in videos are extracted in a two-stage process in order to reduce computational complexity. The following describes operation in some embodiments, although the invention is not so limited. In a first stage, a video signal is monitored at a coarse temporal resolution (see FIG. 9). For instance, the image-based text localizer described in section 4, is only applied to every 20th frame (e.g., frames F80, F100, F120, etc. in FIG. 9). If text is detected (e.g., in frame F120), the second stage oftext tracking will be entered. In this stage, text lines found in the monitor stage are tracked backwards (e.g., frame F119) and forwards (e.g., frame F121) in time up to their first (e.g., frame F115) and last frame of occurrence (e.g., frame F134). This stage uses a combination of signature-based search of text lines and image-based text localization. A signature-based search is less computationally intensive than image-based text localization (section 4). A signature-based search could include comparing edges or areas ofthe text with things in other frames. It could involve an edge map comparison. Horizontal profiles could be 2s compared.

5.1.1 Video Monitoring For Text Occurrences En some embodiments, video is monitored for text occurrences at a coarse temporal resolution. For this purpose, the image-based text localizer might be only applied to an evenly spaced Dame subset of the video. The step size is determined by the objective not to oversee any text line. However, it may be unimportant whether text lines are localized at the beginning, at the middle or at the cad of their temporal occurrence. Lo any case, the text tracking stage will recover the actual tempera l range of each text line.

The maximal possible step size may be given by the minimal assumed temporal duration oftext lines occurrences, which we assume to be one second. Vision research s indicates that humans need between and 3 seconds to process a complete scene. Thus, it seems reasonable to assume that text should appear clearly for at least 2/3 of a second in order to be easily readable. For a 30fps video this translates to a step size of 20 frames.

En some embodiments, if the image-based text localizer does not find any tent line in framer, the monitor process continues with frame+20. If, however, at least one text line I o is found, the image-based text localizer may be applied to frame and frarne+. blent, for each text line in frarneS the algorithm searches for a corresponding text line in frame: 1 and frame+. Correspondence between to text lines may be defined as an area overlap of at least 80% of their respective bounding boxes at their frame locations, although other values could be used. If A and B represents the point set describing the reference and the second bounding box, respectively, then the percentage of overlap may be defined as overlap = IA(1Bl/IA[. Consequently, in this case, two corresponding boxes cannot differ more than 20 percent in size if they occur at the same position in contiguous frames and/or are only allowed to be slightly shifted against each other if they have the same size. Small shins are common for non-static text. If corresponding boxes in framer and framer+ are found for a text box in Dragnet, a new text object (comprising these text boxes) is created and marked for tracking in time. Pseudocode Example 4 gives a summary ofthe video monitoring process.

Video monitoring algorithm for text occurrences (Pseudocode Example 4): (I) video = {frame 0, , frame T} (2) for t = 0 to T step 2/3 seconds (3) localize text in frame t (it) if no text line found (a) continue with next t (6) localize text in frame t - I and t + I (7) for all text lines in frame t which do not belong to any text object yet (8) search for corresponding text line in t - I, t + 1 (9) if search successful (10) create new text object (11) back textobject backward (12) track text object forward s 5.1.2 Text Tracking In some embodiments, each text object is then extended to all frames containing the respective text line based on the information contained in the text objects created in the video monitoring stage. (This reduces the number of bits maps to be provided on conductors 44 in FIG. 1) Text tracking may be performed both backwards and forvards in lo time. However, we restrict our description to forward tracking only since backward tracking does not differ from forward tracking except in the direction you go through the video. The basic idea behind our fast text tracker is to take the text line in the current video frame, calculate a characteristic signature which allows to distinguish this text line from text lines with other contents and search for the image region of same dimension in the next video frame which best matches the reference signature.

The vertical and horizontal projection profile as defined in section 4.4. 2 serve as a compact and characteristic reference signature, although other signatures could be used.

The center of a signature may be defined as the center of the bounding text box of the associated text line. Similarity between two signatures may be measured by signature intersection (e.g., by the sum ofthe minimum between respective elements in the signatures). In cases where signatures capture an object of interest as well as changing background, signature or histogram intersection may outperform Ignores. To find the precise position of a text line in the next frame, all signatures whose centers fall into a search window around the center of the reference signature, may be calculated and compared to the reference signature. If the best match exceeds a minimal required similarity, the text line may be declared to be found and added to the text object. If the best match does not exceed a minimal required similarity, a signature-based drop-out is declared. The size ofthe search radius depends on the maximal assumed velocity oftext.

In our experiments we assumed that text needs at least 2 seconds to move Mom left to right in the video. Given the frame size and the playback rate ofthe video this translates directly to the search radius in pixels. In principle, we could predict the location by the

JO

information contained in the text object so far to narrow down the search space, however, there may be no computational need for it.

Note this signature-based exhaustive search algorithm may resemble the block matching algorithm for motion prediction, except that the similarity measure is based on a signature derived from a feature image of the actual image.

It may happen that the signature-based text line search does not detect a text line fading out slowly since the search is based on the signature of the text line in the previous Same and not on one fixed and derived master/prDtotype signature. The changes from frame to frame might be too small to be detectable. Further, the signature-based text line lo search may fail to track some zooming in or zooming out text. To overcome these limitations, the signature-based search may be replaced every x-th frame by the image based text localizer in order to re-calibrate locations and sizes of the text lines. Newly detected text boxes, however, may be discarded here.

Heuristically, every 5th frame turned out to be a good compromise between speed and reliability, but over intervals could be used. Again, in some embodiments, the bounding boxes of corresponding text lines may overlap by at least 80%.

Due to imperfection in the video signal such as high noise, limited bandwidth (e.g. colors run into each other), text occlusion, compression artifacts, etc. continuous recognition of text objects in the strict sense (e.g. in every frame) is often not possible or so practical. Therefore, it may not be a good idea to terminate tracking if no corresponding text line can be found in the next frame. Rather, tracking should be terminated only if for a certain number of contiguous frames no corresponding text line can be found. For this, two thresholds maXDrgonpourcr-bacd and ma' may be used. Whenever a text object cannot be extended to she next frame, the respective counter may be incremented by one.

Is The respective counter is reset to zero whenever its related search method succeeds. The tracking process may be- aborted, as soon as one of both counters exceeds itS threshold maXDopou,'-bo" or maxD8pO,ia". In our experiments, the threshold for the image-based text localizer was set to ma,CDraogpo-u6'@'6= 3, but other values could be used. This kind of drop outs may be caused by very noisy video frames or temporarily occluded text. The threshold for the signature-based search was set to maXDrtopoucbc = 4, e.g., the distance between two complete localized Dames, but other values may be used. A value of 4 allows for traclclng of text lines where sinare-based search is very difficult such as for zooming in or zooming out text. Pseudocode Example 5, below gives a summary of the video monitoring process, according to some embodiments of the invention. However, other embodiments ofthe invention may be implemented with other details.

Forward text tracking algorithm of a given text object (Pseudocode Example 5): (1) sigBased _ DropOuts= 0 (2) imageBased DropOuts=0 lo (3) while not (beginning or end of video I I sigBased DropOuts > maxSigBased_DropOuts I I imageBased DropOuts ma>; ImageBased DropOuts) (4) get next Dame t () if frmue has to be localized) is (6) localize text in frame t (7) search localized text box that matches to the box in the last frame of the text object (8) if (search successful) (9) add text box to the text object (l l) reset sigbased DropOuts and reset imageBased DropOuts (12) increment imageBased_DropOuts (I 3) else (14) calculate feature image for frame t 2s (15) estimate search area a for the text line (16) create a window w with the dimension ofthe text box in frame t - I (17) get signature sl ofthe text box in t-1 (18) for (each possible position of w in a) (19) calculate signature s2 for w (20) calculate error between s2 and sl (21) memorize minimal error (22) if (minimal error< threshold) (23) add text box to the text object (24) reset sigBased DropOuts as (25) else (26) increment sigBase DropOuts.

5.1.3 Postprocessing To prepare a text object for text segmentation, it may be trimmed down to the part which has been detected with high confidence Therefore, in some embodiments, each text object is temporally trimmed down to the first and last frame in which the image-based text localizer detected the text line. Next, the text object is discarded if, for example, s (1) it occur less than a second or (2) it has a drop-out rate of more than 25%.

Other values could be used. The first condition results Tom our observation that text lines are usually visible for at least one second and shorter text lines are usually false alarms.

The second condition removes the text objects resulting from unstable tracking with which subsequent processing cannot deal. Unstable tracking may be caused by strong compression artifacts or non-text.

Finally, in some embodiments, one or more of the following global features may be determined for each text object. The particular details may vary in different embodiments.

(1) Tex color of text object Assuming that the text color ofthe same text line IS does not change over the course of time, the text color of a text object is determined as the median of all determined text colors per Tome (e.g., as obtained tllro-ugh section 4..3).

The text color does not have to be chosen to be the median. Another average or non average measure could be used.

(2) : The size of the text bounding box may be fixed or change over to time. If it is fixed, we determine its width and height by means of the median over the set of widths and heights.

(3) Text Dosition. The text line might be static in one or both coordinates. A text line is regarded as static in the x and/or y direction if the average movement per frame is less than 0,75 pixels. The average movement is calculated based on the difference in :5 location between the first and last text occurrence ofthat text line normalized by the number of frames.

If the text line is static, we replace all text bounding boxes by the median text bounding box. The median text bounding box is the box which left/right/top/bottom border is the median over all leright/top/bottom borders. If the position is only fixed in one direction such as the x or y axes, the left and right or the top and bottom are replaced by the median value, respectively.

6. Text Sedimentation The text segmentation involves removing backgrounds from text. This is not to be confused with the segmentation of section 4. 4.2.

6.1 Resolution Adiustments. (Note block 30 In FIG. 1.) Text segmentation acts may be performed on resealed images (by, e.g., cubic interpolation) such that the text height of the text object under consideration has a fixed height of, for example, 100 pixel and the Aspect ratio is preserved. The reasons for rc scaling are two-fold: (1) _ Or To ggelg One of the major problems with current text extraction and text recognition in videos is its very low resolution. For MPEG-I encoded videos, individual characters often have a height of less than 12 pixels. Although text is still recognizable for humans at this resolution, it gives todays standard OCR systems a hard time. These OCR systems have been designed to recognize text in documents, which were scanned at a resolution of at least 200dpi to 300dpi resulting in a minimal text height of at least 40 pixels. In order to obtain good results with standard OCR systems it is desirable to enhance the resolution of the text lines.

Enhancing the visible quality oftext bitmaps is another and even more important so reason for up-scaling small text bitmaps. The higher resolution enables sub-pixel precise text alignment (with respect to the original resolution) in section 6.2.2.

(2) Computational savings for Font sizes.

A text height larger than the fixed height (e.g., 100 pixels) does not improve segmentation nor OCR performance. Reducing its size lowers the computational complexity significantly. Note that since our approach is truly multi-resolution and operates on web pages and TV video sequences with a resolution up to 1920 by 1280 pixels, larger font sizes are very likely. 100 pixels is only 1/12 ofthe frame's-height.

6.2 Removing backgrounds (including complex backarounds} As discussed, backgrounds may be removed. (Note block 72 in FIG. 1.) A so complex background has larger variation than a simple background. However, the invention is not limited a particular type of background (it may be complex or simple 2' background). However, as noted above, if particular information is known about the bacl;round oiTthe image, an embodiment of the invention might be modified to use that information.

6 2.1 knaves Text occurrences are supposed to contrast with their background in order to be easily readable. This feature is used here to remove large parts ofthe complex background. In some embodiments, it works as follows, although the invention is not so limited. The basic idea is to increase the text bounding box such that no text pixels fall on! the border and then to take each pixel on the boundary ofthe text bounding box as the seed in to fill all pixels which do not differ more than threshold with the background color.

(Note that in some embodiments, the change ofthe filled pixels to the background color is I firstly only memorized and not actually executed on the bitmap. Execution may be performed after the seed-fills have been applied to all pixels on the box boundary.) The: background color is black for inverse text and white for normal text. Since the pixels on the boundary do not belong to the text and since the text contrasts with its background, the seed-fill algorithm will never remove My character pixels. (Seed-fill algorithms are known in the art.) We call this newly constructed bitmap Br (x,y).

In our experiments, the Euclidean distance between ROB colors was used as the distance function, and the seed fill algorithm used a 4-neighborhood. Moreover, to ensure that all letters are completely contained in the text bounding box, we extended it- I horizontally by 20% and vertically by 40%. Other values could be used.

Not all background pixels need to be deleted, since the sizes of the regions filled by the seed-fill algorithm may be limited by the maximal allowed color difference between a pixel and its border pixel. The size ofthe remaining color regions can be used to fill the remaining regions ofthe background with the background color. Ln some embodiments, each pixel may be as a seed for the seed-fill algorithm. The 8-rleighborhood seed-fiii I algorithm may then be applied hypothetically to Or (x,y) in order to determine the dimension ofthe region that could be filled. Background regions should be smaller then text character regions. Therefore, all regions with a height less than mini pixels and a width less than mind or larger than mamma are deleted, (set to the background color).

6.2.2 Video Images A video text object differs horn a single image text object in the respect that it includes multiple image text representations (e.g., bitmaps) of the same text line and not just one. In some embodiments, the following method is applied to exploit this redundancy toremove the complex background surrounding the actual characters.

However, the invention is not limited to these details. The method can be applied to not only static text, but also to moving text because we have solved the problem of sub-Fixer accurate text line alignment.

The original image may be reloaded in a gray scale format. However, the vector lo quantized version is used to determine which gray scale color is the same as the estimated i text color as described below. l In some embodiments, it works as follows. Assume you pile up the various bitmaps of one text object such that the characters are aligned perfectly to each other.

Pixels belonging to text tend to vary only slightly through time, while pixels belonging to Is non-text (background) often change tremendously through time. Since the texts location is static due to the alignment its pixels are not supposed to change. (Note that even though text is supposed to be static, there may be tiny changes from frame to frame.) Background pixels are very likely to change due to motion in the background or motion of the text line.

We derive a representative text line bitmap for each text object. Given the pile of perfectly-aligned bitmaps, the maximum/minimum operator is applied through time on the! grayscale images for normal/inverse text. Note it is not necessary to use every bitmap of a text object, because the background usually does not change significantly between two consecutive frames. It turned out, that a selection of about 40 temporally evenly spaced frames can be enough to get very good results. For example, if 40 frames are selected and there are 200 frames, then the 40 frame could be spaced by 5. If there are 150 frames, the Dames could be spaced by 15/4, which means the spacing could round up or down to an integer or the spacing could be in consistent, some times 3, but mostly 4 to make l S/4 on average. Note also, some frames at the beginning and end of a text object may be skipped to avoid potential problems with fade in or fade out effects. As suggested above, image so based localization techniques are used every so many Dames to avoid having characteristic text color be changed slowly in fade in or fade out. Signature only tracking could result in the segmentation being mined under such cases.

The following describes how to align the bitmaps essentially perfectly. At first, likewise for images and web pages all bounding text boxes of a text object may be s extended, for example, horizontally by 20% and vertically by 40 /0. Next, all bitmaps may be converted to grayscale since grayscale is more stable to color compression artifacts.

Almost all video compression algorithms represent intensity at a higher resolution than colors such as in the famous 4:2:0 sampling scheme.

Let o(x,y)' ... BN-3(7Y) denote the N bitmaps under consideration and Br(x,y) the lo representative bitmap which is to be derived and which is initialized to Bro(X,y) = Bo(x,y).

As an example, N could be 40 so there are 40 bitmaps from 40 frames. Then, for each I bitmap Bj(x,y), i {1, ..., 39}, we may search for the best displacement (ax, dy) which minimizes the difference between Br(x,y) and B;(x,y) with respect to the text colors, e.g., (dXt Pt, dy Pt) = argmin (B't (x, y) - B. (x + car, y + My)) (r,yeAB_(s,y)=ctrCo/or IS The reason why this kind of block matching search works is because only pixels with text color are taken into account where the text color may be the estimated text color from section 4.4.3. A pixel is defined to have text color if and only if it does not differ more than a certain amount from the text color determined for the text object. Note, that this distance is calculated based on the ROB values. At each iteration, E3r(x7y) is updated from the previously stated equation to Br(x,y) = max (Brj (my), B; (x + dx Pt, y + dy Pt)) for normal text and to Brj(x,y) = min (Brj (x,y), B; (x f dxt Pt, y f dye)) for inverse text.

Note that if a text object has been identified to be static in section 4. 4.3, we do not have to search for the perfect translations. Instead, the translations between the various bitmaps may be all set to null.

Through the process of section 6.2.2, the background may tend to get brighter and brighter for normal text and darker and darker for inverse text. However, it is possible that the first frame is the brightest or darkest respectively.

6.3 Binarization (Noteblock38in FIG I.) The text bitmaps Br(x,y) is now prepared for recognition by standard OCR engines. Hereto, the grayscale text bitmaps may be converted to black on white background. The following describes a way to find a suitable threshold value, one that allows good if not optimal separation between text and background. From section 4.4.3 we know the estimated text color, the dominant background color and whether we have to deal with normal text or inverse text. Since most ofthe background has been removed anyway in section 6.2, we decided to set the background color to black for inverse text and! to white for normal text. Then, a good binarization threshold is to choose the intensity half 0 way between the intensity ofthe text color and the background color. Each pixel in the text bitmap which is higher than the binarization threshold may be set to white fior normal l text and black for inverse text. Each pixel in the text bitmap which is lower or equal than the binarization threshold may be set to black for normal text and white for inverse text.

Finally, it is recommended to clean-up the binary bitmap by discarding small regions (set to the background color) in the same way as described in section 6.2.1.

Additional Information For each of the sections above, the invention is not limited to the particular details mentioned therein.

Some embodiments ofthe present invention are not only able to locate and to segment text occurrences into large binary images, but also to label each pixel within an image or video whether it belongs to text or not. Thus, our text localization and segmentation.techniques can be used for object-based video encoding. Object-based video encoding is known to achieve a much better video quality at a fixed bit rate compared to existing compression technologies. However, in most cases, the problem of extracting objects automatically is not solved yet. Our text localization and text segmentation algorithms solve this problem for text occurrences in videos.

Some embodiments ofthe invention involve a multi-resolution approach in which the text localization and text segmentation algorithm works successfully from MPEG-1 video sequences up to lv'MPEG-2 video sequences (1980x128G) without any parameter adjustment. As an example, character sizes can vary between S pixels and half the Fame height. ?.8

FIG. ] O illustrates a computer system 1 SO having a processor 184 and memory 188.

Memos 188 represents one or more of a variety oftypes of memory apparatus including RAM, hard drives, CD ROMS, and video memory to name only a few; Memory 188 includes machine readable mediums on which instructions can be stored to perform the s various functions described above. Memory 188 can also store data (e.g., digital video signals) to be processed and intermediate and final results of processing. It will be appreciated that FIG. 10 is 1'iglly schematic and in practice would include a variety of other well l;nown components.

The term frame is intended to have a broad meaning. For example, it does not 0 matter whether it is interleaved or not. Likewise, the terms image and video are intended to be interpreted broadly. No particular fonnat is required.

If the specification states a co.m.ponent, feature, sh acture, or characteristic "may", "might", or "could" be included, that particular component, feature, structure, or characteristic is not required to be included. If the specification or claim refers to "a" or is "an" element, that does not mean there is only one ofthe element. If the specification or claims refer to "an additional" element, that does not preclude there being, more than one of the additional element.

Those skilled in the art having the benefit ofthis disclosure will appreciate that many other Variations from the foregoing description and drawings may be made within so the scope of the present invention. Indeed, the invention is not lirnted to the details described above. Rather, it is the following claims including any amendments thereto that define the scope of the invention.

Claims (1)

1 A method com prosing a receiving a text object including bounding boxes of multiple frames of a video signal; b estimating a color of text of the bounding boxes; and c aligning blocks representing the bounding boxes through a best displacement search In which only pixels having a color within a threshold of an estimated color are considered 2 The method of claim 1, wherein representative bit maps are updated through the best displacement search 3 The method of claim 1, wherein representative bit maps are updated through results of a minimum displacement equation.

4 An apparatus comprising a machine readable medium having Instructions thereon which when executed cause a processor to perform a method nclua'ing: receiving a text object Including bounding boxes of multiple frames of a video signal, estimating a color of text of the bounding boxes; aligning blocks representing the bounding boxes through a best displacement search in which only pixels having an estimated color are considered.

The apparatus of claim 4, wherein representative bit maps are updated through the best displacement search 6. The apparatus of claim 4, wherein representative bitmaps are updated through results of a minimum displacement equation.

7 A method comprising.

a. receiving digital Images In text bounding boxes; and b. In preparation for a segmentation process, adjusting sizes of the digital Images to a fixed height, wherein If a particular one of the digital Images has a height smaller than the fixed height the dgtaMmage is increased In height and If the particular one of the digital images has a height greater than the fixed height, the digital images Is reduced In height 8 The method of claim 7, wherem multiple ones of the digital video images originate from a largemmage 9 The method of claim 7, wherein the height Is 100 pixels An apparatus comprising a machine readable medium having Instructions thereon which when executed cause a processor to perform a method including a receiving digital images In text bounding boxes, and b. In preparation for a segmentation process, adjusting sizes of the digital images to a fixed height, wherein if a particular one of the digital images has a height smaller than the fixed height the digital image Is increased In height and If the particular one of the digital Images has a height greater than the fixed height, the digital images is reduced In height JO 11. The apparatus of claim 10, wherein multiple ones of the digital video images originate from a larger image 12 The apparatus of claim 10, wherein the fixed height Is 100 pixels.