Document geometric layout analysis based on adaptive threshold

etc. after scanning process, it will show us the result in the image file. These image files will be the input of an recognition system, the output of the recognition system are text files that can be easily edited and archived, such as files of * .doc, * .docx, * .excel, * .pdf, etc. The dissertation focuses on studying the the page analysis steps, in which the focus is the analysis of the geometric structure of the layout. Figure 1: Illustration of basic processing steps of text recognition system 1.1.1. Pre-processing The task of pre-processing a layout is usually binary, defines the components of connected image, filters noise, and aligns the gradient. The output of the pre-processing step will be the input of the page analysis process. As a result, the pre-processing results will also have significant effects on the results of the page analysis. 1.1.2. Document layout analysis Document layout analysis is one of the major components of text recognition systems (OCR - System). Besides, it is also widely used in other fields of computing such as document digitization, automatic data entry, computer vision, etc. The task of page analysis includes automatically detecting image areas on a document layout (physical structure) and categorize them into different data regions such as text area, image, table, header, footer, etc. (logical structure). Page analysis results are used as an input to the recognition and automatic data entry of document imaging processing systems. 1.1.3. Recognition of optical characters This is the most important stage, this stage determines the accuracy of the recognition system. There are many different classification methods applied to word recognition systems, such as: matching method, direct approach method, grammar method, graph method, neural network, statistic method, and support vector machine. 1.1.4. Post-processing This is the final stage of the recognition process. Maybe post-processing is a step to joint the recognized characters into words, sentences, and paragraphs to reconstitute text while detecting false recognized errors by checking spelling based on structure and semantics of words, sentences or paragraphs of text. The discovery of errors, mistakes in recognition at this stage significantly contributed to improving the quality of recognition. Document layout Pre-processing Analysis of the geometric structure Text file Post-processing Recognize Analysis of the logical structure 1.2. The typical algorithms for analyzing page’s geometric structure Over the decades of development so far, there are a lot of page analysis algorithms have been published. Based on the order of algorithms’ execution, document layout analyzing algorithms can be divided into three different directions of approach: top-down, bottom-up and Hybrid methods. 1.2.1. Top-down direction of approach Typical top-down algorithms such as XY Cut, WhiteSpace, etc. These approach algorithms perform page analysis by dividing the document layout into horizontal or vertical directions under spaces in the page. These spaces are usually along the boundary of the column or border of paragraphs. The strength of these algorithms is their low computational complexity, which results in good analysis on rectangular pages, ie, layouts where the image areas can be surrounded by rectangle does not cross. However, they cannot process pages which are non- rectangular image areas. 1.2.2. Bottom-up direction of approach Typical bottom-up algorithms such as Smearing, Docstrum, Voronoi, etc. These approach algorithms start with small areas of the image (pixels or characters) and in turn group the small areas of the same type together to form the image area. The strength of this approach is that algorithms can well process image pages with any structure (rectangle or non-rectangle). The weakness of bottom-up algorithms is that memory is slow, because small areas are grouped together based on distance parameters, which are typically estimated on the entire image page. So these algorithms are often too sensitive to parameter values and over-segmentation of textured image areas, especially font areas with differences in font size and style. 1.2.3. Hybrid direction of approach From the above analysis, the advantage of the bottom-up direction of approach is the disadvantage of the Top-down direction of approach and vice versa. Thus, in recent years there have been many algorithms developed in the hybrid between top-down and bottom-up, one of the typical algorithms such as RAST, Tab-Stop, PAL, etc. Algorithms developed in this direction are often based on analytic objects such as clear space of rectangles, tab stops, etc. to infer the structure of text columns. From there, the image areas are determined by the bottom-up method. The results show that hybrid algorithms have overcome some of the limitations of top-down and bottom-up algorithms, which can be implemented on any document layouts with any structure and less restrictions on distance parameters. However, defining analytic objects is a difficult problem for many reasons, such as having too closely spaced letters, the text area is aligned, left and right are not aligned or the distance between connected components is too large, etc. This has led to the fact that existing algorithms often suffer from forgotten errors or misidentification of analytical paths leading to error analysis. 1.3. Methods and data sets that evaluate the document layout analysis algorithms 1.3.1. Measure Evaluating analysis algorithms for document layout is always a complex issue as it depends on data sets, ground-truths, and evaluation methods. The issue of evaluating the quality of page analysis algorithms has received a lot of attention. In this dissertation, three measures are used: F-Measure, PSET-Measure and PRImA-Measure for all experimental assessments. PRImA- Measure has been successfully used at international page analysis events in 2009, 2011, 2013, 2015 and 2017. 1.3.2. Data In this dissertation, I used three data sets of UW-III, a PRImA data set and a UNLV data set for experimental assessment and comparison of document layout analysis algorithms. The UW-III has 1600 images, PRImA has 305 images, and UNLV has 2000 images. These data sets have a ground-truth at the paragraph level and text level, represented by non-intersecting polygons. The layouts are scanned at 300 DPI resolution and have been re-adjusted the tilt. It contains a variety of layouts on layout styles, which reflect many of the challenges of page analysis. The structure of the layout contains a blend from simple to complex, consists of pictures with text around the layouts, with a large change in font size. Therefore, these are very good data sets to perform comparative analysis of page analysis algorithms. 1.4. Conclusion of chapter This chapter presents an overview of the field of text recognition, in which page analysis is an important step. So far the problem of page analysis is still a problem that many domestic and foreign research interest. There are many recommended page analysis algorithms, especially at international page analysis competitions (ICDAR). However, the algorithms still suffer from two basic errors: over-segmentation and under-segmentation. Therefore, the dissertation will focus on the solutions for the problem of document layout analysis. There are three main approaches for the problem of document layout analysis: top-down, bottom-up and hybrid. In particular, the hybrid approach has been thriving in recent times as it overcomes the disadvantages of both top-down and bottom-up approaches. For that reason, the dissertation will focus more on hybrid algorithms, particularly the techniques for detecting and using analytical objects of hybrid algorithms. The next chapter of the dissertation presents a quick layout background detection technique, this technique will be used as a module in the algorithm proposed in Chapter 3. CHAPTER 2. QUICK ALGORITHM TO DECTECT THE BACKGROUND OF DOCUMENT LAYOUT This chapter presents the advantages and disadvantages of a direction of approach based on the background of layout background in document layout analysis, WhiteSpace page analysis algorithms, fast layout background detection algorithms, and finally experimental results. 2.1. Advantages and disadvantages of the direction of approach based on the background of layout background in document layout analysis On the intuitive aspect, in many cases, the background layout can be detected more easily, and at the same time based on the layout background can easily separate the page layout into different areas. So early on, there were a lot of page analysis algorithms based on the layout background developed, typical example such as X-Y Cut, WhiteSpace-Analysis, WhiteSpace-Cuts, and etc. and recently there are also many algorithms based on the layout developed, for example, Fraunhofer (winning at IC-DAR2009), Jouve (winning at ICDAR2011), PAL (winning at ICDAR2013), etc. The direction of approach based on layout background is not only used in page analysis, but also widely used in the problem of table detection, table structure analysis, and logical structure analysis. The above examples show that the direction of approach based on layout background has many advantages. There are many different algorithms developed for layout background detection, such as X-Y Cuts, WhiteSpace-Analysis, WhiteSpace-Cuts (hereinafter referred to as WhiteSpace), etc. In which, WhiteSpace is known as a well-known geometric algorithm for layout background detection, algorithms are included in the OCROpus open code-source so it is widely used as a basic step to develop algorithm. However, the WhiteSpace algorithm has a very limited execution time which is quite slow, as shown in Figure 2. Thus, acceleration of the WhiteSpace algorithm has many real meanings. 2.2. Layout background detection algorithms (WhiteSpace) for the problem of page analysis Figure 2. Illustration of average execution time of each algorithm. 2.2.1. Definition The largest white space in a layout is defined as the largest rectangle located in the envelope of the layout and does not have any characters, as shown in Figure 3. Figure 3. Blue rectangle represents the largest white space found. 2.2.2. The algorithm for finding the largest white space The algorithm for finding the largest white space (hereinafter referred to as MaxWhitespace) can be applied to objects that are points or rectangles. The key idea of the algorithm is the branch and bound method and the Quicksort algorithm. Figure. 5 a) and 4 illustrate the fake code of algorithm and the step of dividing the rectangle into sub rectangles. In the repository of this dissertation, the input of the algorithm is a set of rectangles (the envelope of characters), the bound rectangle (envelope of whole layout) and the quality function (rectangle), return to area of each rectangle, see Figure 4.a). The algorithm defines a state consisting of a rectangle r, a set of obstacles rectangles (envelope of characters) that reside in the rectangle r and the area of the rectangle r (q = quality (r)). State statei is defined as greater than state statej if quality (ri)> quality (rj). The queue priority is used to store the state. Each algorithm loop will derive state = (q, r, obstacles) as the beginning of the priority queue, which is the state in which the rectangle r has the largest area. If no rectangular obstacles are contained in r then r is the largest rectangular white area found and the algorithm terminates. In contrast, the algorithm will select one of the rectangle obstacles to make pivot, the best choice is as close to the center of the rectangle as possible, see Figure 4.b). We know that the largest white space will not contain any rectangular obstacles so it will not contain the pivot either. Therefore, there are four possibilities which may happens for the largest white space: is the left and the right of the pivot, see Figure 4.c), or the top and bottom of the pivot, see Figure 4.d). Next, the algorithm will identify the rectangle obstacles intersected with each of these sub rectangles, with four sub rectangles r0, r1, r2, r3 generated from the rectangle r, see Figure 5 and calculate the upper bound of the largest possible white space in each newly sub created rectangle, the upper bound mainly selected is the area of each sub rectangle. The sub rectangle along with the obstacles in it and the upper bound corresponding to it are pushed into the priority queue and the above steps are repeated until the state appears with a rectangular r which does not contain any obstacles. This rectangle is the overview solution of the problem to find the largest white space. Figure 4: Describes the step divided layout into four sub-regions of algorithm to find the largest white space, (a) envelope and rectangles, (b) findable pivots, (c, d) left/right and above/below sub-regions. def find_whitespace(bound,rectangles): queue.enqueue(quality(bound),bound,rectangles) while not queue.is_erapty(): (q,r,obstacles) = queue.dequeue_max0 if obstacles==[]: return r pivot = pick(obstacles) r0 = (pivot.xl,r.yG,r.xl,r.yl) rl = (r.x0,r.y0,pivot.x0,r.yl) r2 = (r.x0,pivot.yl,r.xl,r.yl) r3 = (r.x0,r.y0,r.xl,pivot.y0) subrectangles = [r0,rl,r2,r3] for sub_r in subrectangles: sub_q = quality(sub_r) sub_obstacles = [list of u in obstacles if not overlapslu,sub_r)] queue.enqueue(sub_q,sub_r,sub_obStacies} Figure 5: Illustrates the fake code of algorithm to find the largest white space. 2.2.3. Layout background detection algorithm To detect the layout background, algorithm is proposed as a module of the WhiteSpace algorithm applying the MaxWhitespace algorithm to find m-Whitespace (with m - Whitespace of about 300 is sufficient to well describe the layout background), the following background detection algorithm is called WhiteSpaceDetection. Diagram of the algorithm is shown in Figure 5 b). 2.3. Acceleration of layout background detection algorithm To find the white space which cover the layout background, white space detection algorithm recursively divides the layout into sub areas so that the sub area does not contain any characters. When each repeat algorithm will divide each sub area of the layout into four different sub-regions, See Figure 6. This process will form a quadrilateral tree, so if the loop is large then the number of regions that need to be considered will be very large. Therefore, the execution time of the algorithm is very slow. Therefore, in order to accelerate the layout background detection algorithm, it is necessary to minimize the number of subspaces which need to be considered, by limiting the arising of unnecessary sub branch of the quadrilateral tree. Figure 6 shows that the ZG region (the grandparents region) is divided into four sub regions: ZPT sub-region, ZPB sub-region, ZPL left sub-region, and ZPR right sub-region. Continuing to divide the ZPT region, the sub-region must be ZCTR in the ZPR region, so when considering the ZPR region, also consider the ZCTR region, or the ZCTR region to be reconsidered. The example illustrated in Figure 6 shows that the sub-region on the ZCRT of the ZPR region reconsider the ZCTR region. This division process will form a quadrilateral tree and the further downs, the more sub- regions will be reconsidered. In this chapter, the dissertation proposes a solution that minimizes the number of sub- regions being reconsidered. The proposed algorithm (hereinafter referred to as Fast- WhiteSpaceDetection) will not generate sub-regions that lie fully in previous sub-regions, based on the relative position of the pivot of region considering with the pivot of father region. As the example in Figure 6, the ZCTR sub-region will not be generated because it is in the region (ZPR). However, only consider to remove sub-regions in pairs, or left / right sub-regions or above / below sub-regions, in all considered regions. That means, if we consider removing the left / right sub-regions, we will not consider removing the above / below sub-regions, and vice versa, because if we consider the elimination of all four sub-regions, then there will be a space which is never considered, resulting in the omission of some white spaces. For example, in Figure 6, if all the four sub-regions are removed, the ZCTR and ZCRT regions are removed so that some parts of the intersection will be never considered. Thus, the improved Fast-WhiteSpaceDetection algorithm produces the following sub- regions (Figure 7): • Produce above sub-region. • Produce below sub-region. • Produce left sub-region if the left coordinate of its pivot is greater than the left coordinate of the Pivot of the father region and two non-vertical overlapping pivots • Produce right sub-region if the right coordinate of its pivot is less than the right coordinates of the Pivot of the father region and the two pivots are vertically overlapping. 2.4. WhiteSpace algorithm and Fast-WhiteSpace algorithm 2.4.1. WhiteSpace algorithm Analyzing the background structure of the layout is an approach developed by many authors. However, these approaches are difficult to experimentally install, Figure 6: Drawback leading to the decreased speed of white spaces searching by WhiteSpaceDetection algorithm. The ZCTR, ZcRT and its sub-domains will be reviewed multiple times. a) b) Figure 7: Sub-domains generation by WhiteSpaceDection and the Fast-WhiteSpaceDetection algorithms. Figure a) generation of 4 sub-domains by WhiteSpaceDetection algorithm. Figure b) results of sub-domains generation by Fast-WhiteSpaceDetection algorithm. requiring a large number of geometric and detailed data structures with many special cases. Therefore, these methods have not been widely applied. The WhiteSpace algorithm proposed by Breuel can be simply installed without considering special cases. The main steps of the algorithm include:  Step 1 (Figure 8 b): Find and divide interconnected components into three groups based on size: large group includes visual objects, lines, etc. medium group includes characters (CCs) and small group includes interference objects.  Step 2 (Figure 8 c): Find rectangular white spaces.  Step 3: From the white spaces found, filter to obtain vertical white space (vspace) segmenting columns and horizontal rectangle space (hspace) separating segments under some criteria: the size and overlap of white spaces and the density of adjacent characters of the white space.  Step (Figure 8 D): Find text areas by applying white space finding algorithm in step 2. At this point, the CCs are replaced by vspaces and hspaces. a) b) c) d) Figure 8: Steps of WhiteSpace algorithm. Figure a) envelopes of interconnected components (CCs), b) the rectangles are white spaces covering the background of the layout, c) the rectangles are horizontal and vertical segmentation objects used to segment a layout into areas, d) results of segmentation process. 2.4.2. Fast-WhiteSpace algorithm For conducting experiments showing the efficiency (increased speed that does not affect the results of layout background detection) of quick search on the layout. The thesis has applied quick background search module to develop Fast-WhiteSpace and AOSM algorithms (AOSM will be presented in chapter 3). Fast-WhiteSpace is a combination of WhiteSpace algorithm and quick layout background search module. 2.5. Experiment and discussion In this section, we present the results of speed and accuracy comparison between Fast- WhiteSpace and WhiteSpace algorithms on UW-III dataset. Figure 9 a) shows the average execution time on each layout of WhiteSpace and Fast-WhiteSpace algorithms. Both algorithms are tested concurrently on a PC with Intel Pentium 4 processor, 3.4 GHz CPU, 2 GB RAM memory and Windows 7 Ultimate Service Pack 1 operating system. Experimental results show that Fast- WhiteSpace algorithm achieves extremely fast execution speed over the original WhiteSpace algorithm. Figure 9 b) presents the results of accuracy evaluation for algorithms on UW-III dataset with PSET measure. The change in accuracy of Fast-WhiteSpace algorithm is insignificant compared to that of the original algorithm, and shows relatively good results compared to those of modern advanced algorithms, 91.87% of AdWhiteSpace algorithm compared to 93.84% of the Tab-Stop and 79.45% of RAST algorithm. 2.6. Chapter conclusion In this chapter, the advantages gained from layout-based approach have been presented and concretized with evidences of developed powerful layout-based algorithms. In addition, background search algorithm (WhiteSpaceDetection) and quick layout background search algorithm (Fast-WhiteSpaceDetection) have also been illustrated. Experimental results have shown that the improved WhiteSpace algorithm (using Fast-WhiteSpaceDetection as a module) delivers remarkable execution speed and mostly unchanged accuracy in comparison with that of the original algorithm. a) b) Figure 9: Execution time and accuracy of Fast-WhiteSpace algorithm compared to those of WhiteSpace and typical algorithms: a) execution time, b) accuracy. CHAPTER 3. DOCUMENT LAYOUT SEGMENTATION ALGORITHMS HP2S AND AOSM This chapter presents two document layout analysis algorithms: A hybrid paragraph-level page segmentation - hereinafter referred to as HP2S algorithm and an adaptive over-split and merge for page segmentation - hereinafter referred to as AOSM algorithm. The first part presents the layout analysis models of both HP2S and AOSM algorithms. The second part presents the phase of gathering phrases from interconnected components to form text areas of HP2S algorithm. The third part presents the two phases of AOSM algorithm: phase 1: segmenting layouts into candidate text areas, phase 2: gathering small segmented text areas to form text areas. The phase of segmenting text areas into paragraphs is presented in the fourth section. Finally, the experimental results on the data sets of page analysis competitions from 2009, 2015, 2017, UWIII and UNLV data sets will be. 3.1. Page analysis models of HP2S and AOSM algorithms The algorithms analyze the pages in a hybrid approach which is a combination of top- down and bottom-up approaches. In recent years, many powerful algorithms have developed in hybrid approach. The general idea of hybrid approach is to use low-level information (normally interconnected components) to identify segmentation thereby infer column structure of the layout, which means to figure out the number of text columns in the layout and that they will be on different sides of the separators. Then, use gathering method to group low-level components to form text areas. Finally, the text areas are segmented into paragraphs. In this section, the thesis presents the page analysis models of both HP2S and AOSM algorithms, see Figure 10. From model 10, it can be seen that HP2S and AOSM apply the same method of segmenting the text areas into paragraphs. However, two algorithms use two different approaches to identify the text areas, see Figure 11. HP2S uses bottom-up approach to group interconnected components to form text areas while AOSM uses top-down approach to segment the layouts into candidate text areas, then apply the adaptive parameter method to group small segmented text areas. Details of both algorithms are presented in sections. Figure 10: General models of HP2S and AOSM algorithms. Figure 11: Algorithm diagram of both HP2S and AOSM algorithms: a) HP2S algorithm, b) AOSMalgorithm. 3.2. HP2S algorithm In this section, the thesis presents the main steps for determining text areas of HP2S algorithm. This process consists of three main steps as illustrated in Figure 12. In step 1 the algorithm will detect tab - lines between text columns. Step 2, the algorithm uses Hough transform and tab - lines to identify text lines. Finally, the text lines are grouped to form text areas. Details of these steps will be presented in sections , , . 3.2.1. Tab – lines detection Figure 12: Main steps for determining text areas of HP2S algorithm. Tab-Stop algorithm has presented the problem of detecting tab-lines as a sequence of characters at the beginning or the end of each line (tab-stop) and vertically aligned. These segmentation lines can be used to replace physical segmentations or rectangular white spaces in detecting column structure of document layout. In this section, I would like to introduce a simple method for detecting tab-lines. HP2S algorithm has a tab-line detection method which has fewer step, is simpler and easier to experimentally install. 3.2.2. Text lines identification Firstly, Hough transform is performed on the midpoints set of bottom edges of the characters to find the sequence of horizontally aligned characters. The sequence of horizontally aligned characters will be the best candidate to form text lines. Each of these characters sequence is called a candidate text line, see Figures 13 and 14. For each candidate text line, the algorithm will estimate the horizontal spacing of the characters and adjacent words, the spacing between the words is denoted by dw. The dw spacing will be used along with segmentation lines to segment the candidate text lines into text lines as follows: two horizontal adjacent characters are in the same text line if they are not on two sides of a certain segmentation line, and their horizontal spacing does not exceed two times of dw. The combination of segmentation lines and bottom-up traditional method to identify the text lines has helped the algorithm segment the text lines in very close text columns. In some cases the spacing between two columns is almost equal to the spacing between the words in candidate text lines (13a). However, the existence of vertical segmentation lines has helped the algorithm segment the candidate text lines into different text lines in different columns, see Figure 13b). When the text columns are not aligned, there will be no segmentation line and dw parameter will be useful for identifying text lines. In most of these cases, the spacing between the text lines d is greater than the spacing between the words dw (Figure 14). Unlike the traditional bottom-up algorithms, our algorithm does not use just one dw parameter for all candidate text lines. The dw parameter is estimated on each set of characters with similar font size and in the same candidate text line. Thus, this has reduced the text line fragmentation of the algorithm remarkably, especially the text lines in the header (Figure 13b). a) b) Figure 13: Segmentation lines used in the process of identifying text lines. a) candidate text lines. Characters located at different sides of a segmentation line will belong to different text lines. b) The text lines are the results identified by the algorithm. a) b) Figure 14: a) candidate text lines, b) in case of no segmentation line, dw is used to segment characters into text lines. In some cases, for example, the text areas of the references or paragraphs beginning with special characters, the text areas are often aligned and indented compared to special indices and characters. Therefore, the segmentation line will remove the special indices and characters from the text lines. In order to fix this type of error, we first find more candidate tab-stop by applying the same tab-stop search method as the section with the width of the right adjacent rectangle equal to one of the width of the character being considered. Then, the newly found candidate tab-stops which intersect with the left candidate tab-stops identified from the section will be updated as reference tab-stops or special characters denoted by m_tabs. m_tabs are characters that have been separated from the text line due to the appearance of segmentation line. Finally, the algorithm will combine m_tabs with the right adjacent text lines and labeled them as segmentation text lines. The segmentation text lines will be re-used in the section to identify the paragraphs. 3.2.3. Group clusters of text lines into text areas In this section, the process of grouping text lines into text areas will be presented. The bottom-up approach is used to group adjacent text lines to form text areas with any envelope. The set of text lines identified from the previous section is rearranged in order from left to right, from top to bottom. A pair of lines (linei, linej) simultaneously satisfying the following conditions will be grouped into a same text area. a) b) c) Figure 15: a) Original image, b) separation lines, c) defined text areas. { ( ) ( ) ( ) ( ) ( ) ( ) ( ) | | ( ) Among the above conditions, DisHoriz (.,.) is the horizontal distance between the lines. AvgHoriz is the horizontal average distance of the text lines. yi and yj are ordinates of the centers of text lines linei and linej respectively. x - heightij is the smallest value of x - heighi and x - heighj. CheckTabline (.,.) returns to the true value if two text lines are on two sides of the lines of any separation line; if not, it will return to the false value. CheckRulling (.,.) returns to the true value if two text lines are on two different sides of a horizontal line; if not, it will return to the false value. Conditions (i) and (ii) ensure separating the lines into different columns. This is done by using a combination of separation lines and strict grouping conditions. Condition (iv) allows grouping only text lines of the similar font size and overlapping text lines vertically. It`s worthwhile that the condition (iv) advocates text lines of similar font size and becomes strict when font sizes are different. In another aspect, the distance between the centers of two lines on the left of (iv) includes the large font size while the left side of (iv) includes the small font size. The empirical results shown in Figure 26 show that HP2S is less sensitive to values of parameter .... The most appropriate ... Value is between 1.4 and 1.6. Therefore, the algorithm uses a default value of 1.5 for all experiments. 3.3. AOSM algorithm In this section, the dissertation presents the text area identifying process of the AOSM algorithm. This process consists of two main phases as follows (Figure 16): • Phase 1: Over splitting the image page into candidate text areas. • Phase 2: Group over-split candidate text areas into text areas. Figure 16: Main steps of the text area identifying process of the AOSM algorithm. 3.3.1. Identifying candidate text areas The most common separations applied by one of the leading hybrid algorithms at present are whitespace rectangles, such as RAST algorithm, Fraunhofer algorithm, or strings of white zones, such as PAL or strings of characters at the beginning or end of a line, such as Tab-Stop, ETIPA. Methods based on these separators depend on two steps: • Step 1: extracting candidate separators, • Step 2: selecting and grouping candidate separators into the best separation lines. Thus, hybird algorithms are often sensitive to the result of the process of detecting separation objects. If identified as missing, the under-segmentation error will occur, which if misidentified, the over-segmentation error will occur. To overcome the disadvantages of separation identifying steps, we use a simple and effective solution as follow: • Step 1: To extract candidate separators (white space) we use the WhiteSpace algorithm, which is a simple and effective algorithm, especially this algorithm has had open source codes. • Step 2: The set of candidate white space areas detected in step 1 are used as separators to divide the image page into candidate text areas. With this approach, the AOSM algorithm can overcome the shortcomings, disadvantages of the most powerful separation detection methods at present. Interestingly, the candidate text areas are very easily determined by eliminating separable objects, see Figures. 17c) and 17d). At the same time, the analysis results when using all white spaces will overcome almost completely under-segmentation errors caused by the proximity of image pages or the page structure . However, some text areas may be over-segmented, as shown in Figure 17. These over-segmented text areas can be controlled and corrected in phase 2 of the AOSM algorithm. Phase 1: Oversegmentation Filtering interconnected components Detecting separators Identifying candidate text areas Phase 2: Grouping Identifying text lines Grouping text lines into text areas Figure 17: Illustration of results of steps in the Phase 1: a) original image input; b) rectangles representing detected white spaces; c) the results of “ink pouring” process; d) candidate text areas. 3.3.2. Grouping over-segmented text areas Areas where the number of text lines is small are considered to get over-segmentation error. All text lines in these text areas will be re-grouped together by the adaptive parameter method that is stated as follows: Two text lines linei and linej (belonging to two adjacent text areas) are considered for grouping into a region if the following conditions are satisfied (see Figure 18). { ( ) | | ( ) and are ordinates of the center of text lines: and respectively, is the height of the most appearing characters in a text line, is the smallest of two text lines. Parameter is used to determine the vertical adjacent distance vertically between two text lines in the same image area. These conditions mean that two text lines will be grouped in the same region if they are close enough in the horizontal direction (i) or close enough in the vertical direction (ii). A very worthwhile issue is that condition (ii) allows the algorithm not only to measure the vertical distance between the lines, but also to evaluate the difference in font size between the the text lines. Condition (ii) advocates grouping two text lines of similar font sizes and becomes stricter fonts for text lines which are much different in the font sizes. Experimental results have shown that the AOSM algorithm is less sensitive to parameter (Figure 26) and that the appropriate values of are between 1.4 and 1.6. Therefore, the default value of 1.5 was chosen in all experiments. The value 1.5 corresponds to the 1.5 line spacing for the height of text lines of many text formats. Figure 18: Illustration of the adaptive parameter method. The vertical distance between two text lines, linei,linej is greater than the vertical distance between two lines, linej, linek. However, the two lines, linei,linej are considered in the same group because | | ( ) while two lines, are not in a same group because | | ( ) Figure 19 shows an example of a group of text lines that are over-segmented into text areas. Text lines in the header area are often over-segmented because of the large distance between text lines. The AOSM algorithm will group these text lines into the same text area based on the similarity of the height and the distance relationship between them. Text lines in the header area and in the content section are not grouped together due to the large correlative distance between the centers of text lines. a) b) Figure 19 Illustrate the result of grouping over-segmentation text regions: a) over-segmentation; b) results after clustering. 3.4. Identifying paragraphs 3.4.1. Definition of separation text lines To separate text areas into paragraphs, the HP2S and AOSM algorithms use five types of separation text lines as illustrated in Figure 20. Figure 20: The “dash line” rectangles present for the defined separation text lines. 3.4.2. Split plain text areas into paragraphs Difficulties in analyzing text pages are not only the complex structure of the image page or the change of font style or size, but also the too close distance between text areas. The distance between text lines is sometimes smaller than the distance between the words on the same line. This is a challenge for most page analyzing algorithms that rely on separation objects and the analysis of interrelated components fails. To overcome this difficulty, the HP2S and AOSM algorithms use a set of separation text lines to segment text areas into paragraphs. The paragraph identifying process is as follows: the algorithm browses each text area from top to bottom and from bottom to top so that it does not break through the separating lines (Figure 20.b) or 20.c), and then subdivide each area into smaller areas (Step 1 in Figure 21.d). And then the text lines in these subdivided areas are rearranged in both vertical and horizontal orders, (step 2 in Figure 21.d). Finally, paragraphs are identified by using the separation text lines in Figures 20.a), 20d) or 20.e) (step 3 in Figure 21.d). As illustrated in Figure 21, separation text lines have shown the effectiveness in separating text areas that are similar in the font size, very close together and structurally complex. Traditional top-down and bottom-up algorithms almost fail in this case. a) b) c) d) Figure 21: Splitting plain text areas into paragraphs: a) results of the separation with no use of separation lines, b) “bold” text lines are separation lines, d) text areas are separated by the use of separation lines, e) final separation result. 3.5. Experiment and discussion 3.5.1. Algorithms, data sets, and mesurement In this section, the dissertation presents the experimental results of the HP2S, AOSM and Fast-AOSM algorithms (Fast-AOSM is the AOSM algorithm using the fast white space detecting module as stated in Chapter 2) with the algorithms representing the approaches of top page analyzing algorithms, page analyzing systems in ICDAR2009, ICDAR2015 and ICDAR2015 international page analyzing competitions, commercial products and famous systems of open source codes. • • Typical algorithms for to-down, bottom-up and hybrid approaches include : Docstrum, Voronoi , WhiteSpace, Tab-Stop . • Top systems in ICDAR2009, ICDAR2015 and ICDAR2017 international page analyzing competitions. • Famous commercial products: Fine Reader 8.1, 10, 11 and 12 hereafter are respectively symbolized as: FRE 8.1, FRE 10, FRE 11, FRE 12. • Famous Open Source Systems: OCRopus 0.3.1, Tesseract 3.02, Tesseract 3.03, Tesseract 3.04 Experimental results were performed on well known data sets: UWIII, UNLV, ICDAR2009 dataset, ICDAR2015 dataset and ICDAR2017 dataset. We used measurements including F- Measure, PSET and PRImA measurements in different assessment contexts to evaluate the success of page analyzing algorithms at ICDA2009, ICDAR2011, ICDAR2013 and ICDAR2015 competitions. 3.5.2. Experimental results and discussion Figure 22: The experimental result of the HP2S and AOSM algorithms on ICDAR2009 dataset compared to the top algorithms of the competition in 2009, a) result with F-Measure, b) result with PRImA-measure. Figure 23: The accuracy of the HP2S and Fast-AOSM algorithms compared to the top results published at the ICDAR2015, ICDAR2017 competitions, which is performed on the context of PRImA-measure. a) the result on UNLV dataset, b) result on ICDAR2017 dataset. The accuracy of the Docstrum, Voronoi, WhiteSpace, Tab-Stop and AOSM algorithms on the two datasets is shown in figure 25. Because pages of the UW-III dataset have fairly simple layout (mostly rectangular layout), so most algorithms have fairly high accuracy results, in which the accuracy of Docstrum is 92.87%, and of Tab-Stop is 90.42%. Most of these algorithms’ error is over-segmentation of header with large font sizes. With adaptive parameter, the HP2S and AOSM algorithm almost completely overcomes this error, and increase the algorithm accuracy up to 93.95% and 93.12% compared to 92.87% of Docstrum, illustrated in Figure 25. a) b) Figure 24: The accuracy and error types of Fast-AOSM algorithm compared to the top algorithms in the 2015 competition when performed on ICDAR2015 dataset. a) the accuracy, b) the error types on OCR context Figure 25: Comparison the accuracy and error types of HP2S and AOSM algorithms with typical algorithms with PSET-measure. a) the accuracy of algorithms on two datasets UW-III and ICDAR2009. b) the different error types on ICDAR2009 dataset. The HP2S and AOSM algorithms can overcome almost all over-segmentation error that occurs in header text region, which is an impressive result. However, the accuracy percentage increases slightly compared to the Docstrum algorithm, this is due to the fact that the number of document images having titles with the large font size in the UW-III image set is not many, at the same time, the number of text lines in these region is usually fewer than the majority. The ICDAR2009 dataset has a wide variety of pages’ layout, ranging from simple to complex, the font sizes and theme fonts in the same page change frequently and there are many challenging scenarios that most algorithms still cannot overcome. On a much harder dataset, the HP2S and AOSM algorithms have shown great improvement over the rest of algorithms, in which: the accuracy of the HP2S is 91.84% and of the AOSM is 86.43% compared to of Tab-Stop, second-best algorithm, is 76.68% (Figure 25). The evaluation result with PRImA- measure also shows that HP2S and AOSM outperformed other algorithms: 92.72% for HP2S and 92.63% for AOSM, compared to 82.37% for the second-best algorithm, Fraunhofer. (Figure 22). Figure 25 b) presents the typically error types that algorithms often encounter. The complexity of ICDAR2009 dataset has made it difficult for the algorithm to detect threshold parameters as well as to detect delimiters. Most algorithms fail to reduce both over-segmentation and under- segmentation errors, for example Docstrum has the lowest over-segmentation error (split) at 3.16% and the most serious under-segmentation (merge) at 26.02%, the corresponding numbers of Tab-Stop are 6.11% of split error and 17.07% of merge error. The AOSM algorithm reduces the merge, split errors to 9.17% and 4.28% respectively. Figure 23 shows the accuracy of HP2S and Fast-AOSM algorithms against the top systems at the International Document Layout Analysis competitions in 2015 and 2017. On the UNLV data set, HP2S and Fast-AOSM algorithms stood in the fourth and second place respectively. AOSM algorithm was third on ICDAR2017 dataset in “text” context. The MHS system ranked first in both 2015 and 2017 on all contexts. Figure 24 illustrates the accuracy and error types: merge, miss/partial misses, misclassification and false detection of Fast-AOSM algorithm with the results of the 2015 Document Layout Analysis competition. The Fast- AOSM ranks third behind ISPL and MHS system. It can be seen that Fast-AOSM algorithm reduces both types of error: over-segmentation and under- segmentation much better than other algorithms, which is 17.35% of merge errors and 5.18% of split errors of Fast-AOSM algorithms compared to 18.5% of merge errors and 5.63% of split errors of top system MHS. However, the accuracy of the Fast-AOSM algorithm is lower than the MHS system. This is because MHS includes good modules that detect image region and table region, so the miss/partial misses errors are very low, which is 0.26% of miss/partial misses compared to 17.58% of miss/partial misses for Fast-AOSM algorithms. For the adaptive threshold parameter θ used in combining the two lines together, which has been presented in section. We performed experiments with θ values, ranging from 1.0 to 2.0, on the ICDAR2009 dataset. As we can see in Figure 26, the results of HP2S and AOSM algorithm does not change sensitively to the θ value:. The less sensitivity of parameter θ to group conditions is based on the fact that the difference in font sizes was partly reflected in the calculation of the distance between the center of the two lines and the threshold distance which is based on minimum x- height of the two lines. In other words, the HP2S and AOSM algorithms support grouping two lines with the same font size and are strict in the opposite case, even if they are very close to each other. Figure 27 shows the average running time on each page of the Docstrum, Voronoi, WhiteSpace, TabStop, HP2S, AOSM and Fast-AOSM algorithms on the ICDAR2009 dataset. The experiment was performed on a personal Computer with specifications of Intel Core i5 Processor 3.2GHz. AOSM takes an average of 1 second to process an image, is almost equivalent to the WhiteSpace algorithm, faster than Voronoi and slower than Docstrum. HP2S has a faster average execution time than the Voronoi, WhiteSpace, Tab-Stop and AOSM algorithms. Fast-AOSM algorithms have a slower execution time than Docstrum and are faster than the rest of the algorithms. a) b) Figure 26: Execution time of HP2S and AOSM algorithms on PSET-measure, ICDAR2009 dataset with different θ values. Figure 27: Average execution time of each algorithm on each page. 3.6. Chapter’s conclusion In this chapter, we have presented an AOSM algorithm (Adaptive Over-Split and Merge) for analyzing document layout. The goal of the AOSM algorithm is to reduce at the same time both the most common types of errors in document layout analysis algorithms: under-segmentation and over-segmentation that are caused by changes in font sizes and theme fonts, close distance between text regions and the complex layout of the page. First, AOSM uses the set of all white areas covering background document as delimiters, which is an interesting and effective way compared to other common separating methods, such as tab-stops or whitespaces to find out the column layout of page. This strategy not only solved the problems of detecting delimiters, but also effectively solved the problem of under-segmentation. Over-segmentation errors are often caused by a large variation in font size, theme fonts and spacing between large text. The adaptive parameter method of AOSM overcomes the problem of over-segmentation in text of the same region and the over-segmentation problem occurs on the same line. Finally, the text only region are separated into paragraphs using text delimiter lines. CONCLUSION AND FURTHER RESEARCH Conclusion With the set objectives, the thesis has achieved the following main results: 1. Evaluating, comparing typical document layout analysis algorithms on PRImA datasets and Vietnamese datasets. The results provided the most comprehensive overview of layout analysis, strengths and weaknesses of the approaches. This will serve as a guideline for future research objectives. This results are published in [4]. 2. Propose a solution for accelerating the background image detection algorithm by applying Branch and Bound to limit the number of unnecessary branches to be considered thus speeding up the execution of the algorithm. This results are published in [3]. 3. Propose new solutions in the detection and use of delimiters. Propose adaptive parameter method for the clustering process of the bottom-up approach. Define delimiter lines to successfully separate the text region into paragraphs. Related results are published in [1, 2, 5, 6]. Further research Although the thesis has achieved certain results, the research results mainly focus on separating the text region from the non-text region, separating the text region into paragraphs. Problems such as: image region, table region, chart region, logical structure analysis, etc. are not mentioned in the thesis. In the future, the thesis will continue to develop in the following directions: 1. Detecting image region 2. Analyzing logical structure 3. Detecting and analyzing table layout LIST OF PUBLIC WORKS OF AUTHOR 1. Ha Dai-Ton, Nguyen Duc-Dung and Le Duc-Hieu, Free parameter for document layout analysis, The 7th National Conference on Basic Research and Application of Information Technology (FAIR2014), 2014. 2. Ha Dai Ton, Nguyen Duc Dung and Le Duc Hieu, Over-Splitted and Merged for Geometry Document Layout Analysis, The 8th National Conference on Basic Research and Application of Information Technology (FAIR2015), 2015. 3. Ha Dai Ton, Nguyen Duc Dung, Improving document layout separating algorithm by background structure analysis, The 19th National Workshop: Selected issues of Information Technology and Communication (@2016), pp. 49-53, 2016. 4. Ha Dai-Ton, Nguyen Duc-Dung and Le Duc-Hieu, Comparison and assessment of image separating algorithm, Journal of Natural Science and Technology, Thai Nguyen University, Vol. 120, No. 06, pp. 03-08, 2014. 5. Ha Dai Ton, Nguyen Duc Dung, A hybrid paragraph-level page segmentation, Journal of Computer Science and Cybernetics, Vol 32, No 02, pp. 153-167, 2016. 6. Ha Dai-Ton, Nguyen Duc-Dung and Le Duc-Hieu, An adaptive over-split and merge algorithm for page segmentation, Pattern Recognition Letters, Vol 80, pp. 137-143, 2016.