Spotting Separator Points at Line Terminals in Compressed Document Images for Text-line Segmentation

Transcription

1 Spotting Seprtor Points t Line Terminls in Compressed Document Imges for Text-line Segmenttion Amrnth R. Deprtment of Studies in Computer Science, University of Mysore, Indi. ABSTRACT Line seprtors re used to segregte text-lines from one nother in document imge nlysis. Finding the seprtor points t every line terminl in document imge would enle text-line segmenttion. In prticulr, identifying the seprtors in hndwritten text could e thrilling exercise. Oviously it would e chllenging to perform this in the compressed version of document imge nd tht is the proposed ojective in this reserch. Such n effort would prevent the computtionl urden of decompressing document for text-line segmenttion. Since document imges re generlly compressed using run length encoding (RLE) technique s per the CCITT stndrds, the first column in the RLE will e white column. The vlue (depth) in the white column is very low when prticulr line is text line nd the depth could e lrger t the point of text line seprtion. A longer consecutive sequence of such lrger depth should indicte the gp etween the text lines, which provides the seprtor region. In cse of over seprtion nd under seprtion issues, corrective ctions such s deletion nd insertion re suggested respectively. An extensive experimenttion is conducted on the compressed imges of the enchmrk dtsets of ICDAR13 nd Alirez et l [17] to demonstrte the efficcy. Generl Terms Line seprtor points t every line terminl in compressed hndwritten document imges enling text line segmenttion. Keywords Line seprtors, Document imge nlysis, Hndwritten text, Compression nd decompression, RLE, CCITT. 1. INTRODUCTION Generlly, document imge is represented in compressed formt. The formt is developed sed on the guidelines of CCITT Group 4 stndrds, which is prt of ITU (Interntionl Telegrph Union) [1]. This compression stndrd fcilittes oth efficient storge nd trnsmission [12] nd therefore it is utilized in rel-time pplictions including fx mchines, photocopy mchines, digitl lirries nd communiction networks. The compressed representtion of document could imply solution to the ig dt prolems rising from the document imges, prticulrly with regrd to storge nd trnsmission. However to perform digitl document nlysis (DDA) [20], the imge in the compressed formt hs to undergo the decompression stge [13, ]. This pre-requisite wrrnts dditionl uffer spce nd lso extr time. If DDA could e crried out directly in the compressed version, then the document imge compression could e viewed s n effective P. Nghushn Deprtment of Studies in Computer Science, University of Mysore, Indi. solution to the ig dt prolem rising from the document imges. Few literture reported working directly on the compressed version of the printed text. But the chllenging jo is to perform DDA on the hndwritten imges ecuse of oscilltory vritions, inclined orienttions nd frequent touching of text lines while scriing the text lines. Therefore, performing the segmenttion in n uncompressed hndwritten text could e difficult tsk. However, we foresee this possiility in view of the literture presented y Jved et l [13] which reports the reserch effort in the compressed printed document. In this reserch pper, the proposl is to spot the seprtor points t every line terminl in the compressed hndwritten imges enling text line segmenttion. The CCITT Group-3 / Group-4 nd JBIG protocols re developed sed on the run-length encoding (RLE) [15], widely ccepted for inry document compression. The RLE of the document imge is represented in mtrix formt. The first column of the mtrix lwys strts with white spce. This represents the left mrgin. The depth (length) of the white spce is lrger t the seprtion points compred to the depth of the white spce when it encounters the text lines. However, the lst column of RLE does not infer the depth of right mrgin of the document. So we crete virtul column contining lst non-zero vlue of every row of the RLE dt. In summry, we propose to mke use of single column inorder to find the seprtor points t every line terminl. If the depth of the white spce in the first column is equl to tht of the document width, then it certinly infers text-line segmenttion. But most of the time this sitution my not occur in cse of hndwritten texts. This is ecuse of oscilltory vritions, inclined orienttions nd frequent touching of consecutive lines, prticulrly while writing on the white pge (un-ruled pper). Further, every text-line in hndwritten document does not necessrily strt or end with sme left/right mrgin spce. Such sitution is more evident in the first nd the lst text-lines in every prgrph. The depth of the white spce would e lrger t the point of seprtion thn the depth elsewhere, ut this depth my not e pronounced even t the expected seprtor points when two text lines re touching t the eginning of the line itself nd thus it cuses under seprtion. So the correction is to insert seprtor points. Similrly deeper mrgin for consecutive text lines could lso cuse under seprtion, y showing the entire stretch s one seprtion. On the other hnd, lrger concvity in the chrcter, higher indent spce, nd disjoint composition of chrcter my result in perceivly high depth nd hence pseudo seprtion, cusing over seprtion. Therefore, the correction requires the deletion of such over seprtion points. 40

2 The orgnistion of the pper is s follows Section 2 contins relted reserch work. Section 3 includes n understnding of the RLE structure. The lgorithmic model of the proposed method is explined in Section 4. Experimentl nlysis conducted on enchmrk dtsets is presented in Section 5. Summry nd future possiilities re presented in Section RELATED WORK In spite of the extensive serch, there is no contriution reported in the field of DDA directly operting we could identify some relted works pertining to the compressed imge processing. Most of the contriutions re in the field of skew detection / correction, document mtching nd rchivl. The overview of this literture is covered in Tle 1. All the literture ppers presented in this tle refer to some DDA on the compressed printed document imges. In summry, the motivtion is the sence of the work on the compressed version of hndwritten document, nd the hope tht cn e trced prticulrly ecuse of [8,13,14]. Tle 1. Relted reserch work Reserch Are Authors Contriution Interntionl Journl of Computer Applictions ( ) 3. COMPRESSED IMAGE REPRESENTATION AND TERMINOLOGIES The CCITT Group 3 [2] or Modified Huffmn (MH) [15] imge formt primrily uses line y line coding technique. Bsiclly the MH uses RLE s its sis encoding function. RLE descries the length of the run tht crries similr pixel vlue which is either 0 or 1. The pixel crrying vlue 1 (on) is interpreted s foreground wheres the pixel crrying the vlue 0 (off) is considered s ckground. An exmple of RLE formt is represented in the tle 2. The RLE consists of lternte columns of numer of runs of 0 nd 1 cknowledged s odd columns (1, 3, 5, ) nd even columns (2, 4, 6, ) respectively. The column lwys strts with white runs. In senti of white run t the strting point tht is in the first column, it is essentil to mke n entry s 0 (note the line 7 nd 8 in Tle 2). Further this tle shows how the RLE compression technique is involved in shrinking inry imge dt of length sy 14 its to 5 columns. Ech vlue in the RLE represents the mgnitude or depth of the corresponding runs. Skew detection / correction in CCITT Group 4 Shuln Deng et l [3] Exploiting 2-dimensionl correltion etween scn lines y extrcting connected component. Employed occurrence frequency of word ojects Skew detection on Run dt Y. Shim et l [5] Coordinte trnsformtion sed on projection profile method. Skew detection Directly on compressed CCITT Group 4 A.L. Spitz [6] Used position loctions of lck nd white structures to determine skew ngles. Skew detection in JBIG J. Kni et l [7] Used projection profile for predicting skews Oject Identifiction C. M [4] Lyout Anlysis E. Regentov et l [9] Document Retrievl J. J. Hull [10, 11] Document Retrievl Yue Lu et l [12] Segmenttion Mohmmed Jved et l [8, 13, 14] Attempted in identifying r code directly in compressed CCITT Group 4 imges. A prticulr pttern from reltive position of pixels etween scn lines were used. Used the connected-component-detection nd lelling techniques on JBIGencoded imges for otining glol lyout Used psscode of CCITT Group 4 s feture vectors. He used Husdorff distnce mesure for document mtching Hve worked on connected component techniques of CCITT Group 4 stndrd imges. Word ojects re ounded y extrcting chnging elements. These word ojects re mtched sed on weighted Husdorff distnce Hve performed Line, Word, nd Chrcter Segments directly from runlength compressed dt. They hve used horizontl projection profiled nd locl minim points to estimte the text lines. Tle 2. Binry imge dt [13] Line Binry dt

3 For etter understnding, figure 1 () nd () show portion of the smple document imge nd its compressed version. Fig. 2 shows the RLE structure of this smple imge. 3.1 Depth of the White Spce The vlues in the first column of RLE represent the depth of the white spce strting from the left order of the document pge. Fig 3 shows the depth projection for portion of the first column extrcted from figure 2. It is oserved tht the entries in the first column re non-zero nd this indictes minimum white spce s the left mrgin, even in the presence of the text-line. () Uncompressed document () Compressed document Fig. 1: Length pttern oserved from compressed text line (Reference: A portion of ICDAR13 test imge -214.tif) Fig. 3: The depth of the white spce from left end of the document () The first column of the RLE, () Projection of vlues The first column of the RLE implies the left mrgin of the document, wheres in cse of the right mrgin the depth of the white spce hs to e trced in the RLE ecuse it is not ville s column. Here, the lst non-zero entry of every row of RLE is considered s the right mrgin of the document nd hence virtul column is uilt. An illustrtion is provided in fig 4 where the lst non-zero entries re tken from the odd column of every row. In some cses, the lst non-zero entry ppers in n even column nd so zero entry should e dded for the virtul column of the corresponding row. A lst non-zero entry in the even column indictes tht the text-line touches the right order of the document. Fig. 2: The RLE Structure (Reference: A portion of ICDAR13 test imge -214.tif) Fig. 4: The depth of the white spce from right end () RLE formt, () Virtul column 42

4 3.2 Under Seprtion One of the resons for under seprtion is the touching or overlpping of two text-lines t the strting point itself. Here, the depth of the white-run is resonly low. Fig 5 () shows n exmple where the text-lines 3 nd 4 re touching ech other t the eginning of the text line. The other reson is when lrge mrgin spce is indented t the eginning of the text line. This is shown in Fig 5 () where the second text-line hs more left mrgin white spce compred to other text-lines. 4.1 Finding the nds of consecutive rows with lrger white depths The gol of this method is to identify the seprtor (non-text) nd non-seprtor (text) regions. The first column of RLE nd threshold re the inputs. The threshold vlue (t) is heuristiclly chosen s 1/25 of the document width. This threshold is considered fter nlysing the other thresholds including 1/35 nd 1/15 s well. Initilly, we remove the mrgin spce from the left order of the document y sutrcting the vlues with minimum vlue. After this elimintion process, if the vlue is greter thn the threshold, then the corresponding index position is lelled s seprtor point (sy 1 ), otherwise it is presumed s text region (sy 0 ). Fig 7 () shows smple imge mrked with seprtor nds (lck ptches) long the left order of the document. Fig 7 () shows the periodicity of the seprtor nds. Fig. 5: Under Seprtion (Reference: A portion of ICDAR13 test imges tif nd 220.tif) () Touching of lines t the strting point, () Text line with more spce for left mrgin 3.3 Over Seprtion The over seprtion occurs when text line is identified s non-text (white spce) region. Fig 6 shows the chrcter J cusing pseudo seprtion point. The over seprtion is due to concvity of the chrcter from the left end. The other ffecting fctor could e the multiple disjoint frctions or components which compose chrcter. Fig. 6: Over Seprtion (Reference: A portion of ICDAR13 test imge tif) 4. IDENTIFICATION OF TEXT-LINE SEPARATORS IN COMPRESSED IMAGES From the detils presented in Section 3, there re three min stges () Finding the nds of consecutive rows with lrger white depths, () Finding the under seprtion (c) Finding the over seprtion. A detiled explntion is provided for ech stge in the following su sections. Fig. 7: Formtion of Seprtor nds (Reference: A ICDAR13 test imge tif) () Seprtor nds, () Periodicity of the seprtor nd The first column of RLE nd the threshold re represented s FC nd t respectively in the lgorithm. The finl output, sy Seprtor Bnd (SB), represents the region of text line seprtion. Algorithm CretingSeprtorBnds Input: Output: The time complexity of finding the minimum vlue is, where m = size of the first column. The lgorithm scns the input rry once gin to find the seprtor points. Overll, the worst cse of the lgorithm is. 43

5 4.2 Finding the under seprtion The two fctors cusing under seprtion hve een detiled in Section 3.2. In this section, we del with the seprtor nd width which is reltively lrge. The under seprtor region could e seen in Fig 8. Fig 5() in Section 3 shows the region of interest (ROI). When seprtor nd width is two times lrger thn tht of n verge nd width, then it is presumed s under seprtor region or ROI. To resolve this, the ROI would e recursively iterted with the sme lgorithm descried in the previous section. The recursion termintes when no ROI is detected. On the other hnd, the seprtor nd width would e extremely lrge, sometimes it my cover more thn 1/10 of the document height, which definitely ffects the verge seprtor nd width. This scenrio is shown in Fig 9. So we directly tke this region s ROI nd this would not e considered for clculting the verge. The threshold 1/10 is chosen heuristiclly sed on the verge numer of the text lines in the dtset. Algorithm FindingUnderSeprtion Input: Output: Fig. 8: Seprtion nd nd frequency (Reference: ICDAR13 test imge tif) The time complexity for finding the verge seprtor nd width is. The detection of the ROI is. The worst cse scenrio for this lgorithm is. Next, the seprtor points re identified y tking the mid position of ech nd with respect to its position. Suppose the strting nd the ending position of seprtor nd re respectively, then the mid-point is computed s seprtor exmple.. Fig. 10 shows the line Fig. 9: Seprtor nds (Reference: A ICDAR13 test imge tif) () A lrger seprtion nd, () Seprtion nds fter Itertion Fig. 10: Line Seprtors (Reference: A portion of ICDAR13 test imge tif) 44

6 The other under seprtion prolem is illustrted in Section 3.2 (Fig 5()) when two djcent text lines re touching t the eginning of the text-line. We nlyze the frequency of the seprtor points. If the gp etween the two djcent seprtor points is more thn twice its verge gp, then it is considered s n under seprtion region. The under seprtion region could e seen in fig 11 with its corresponding seprtor frequency. The over seprtion points re detected when the gp etween the djcent is lesser thn 1/3 of the verge gp. In Fig 11, the gp etween the seprtor points 6 nd 7 is identified s over seprtion. In this scenrio, the seprtor point 6 is to e removed ecuse this seprtor point is comprtively closer to its djcent point 5 thn the gp etween 7 nd 8. The mthemticl model is given elow. Fig. 11: Under seprtion nd frequency (Reference: A portion of ICDAR13 test imge tif) To resolve this, first we compute the verge seprtion gp etween the djcent seprtors. Next, we re-compute the verge seprtion gp y ignoring the touching seprtor points. This newly computed verge is used in-order to insert the seprtor exctly in the midpoint of the two touching textlines. The sme lgorithm to identify the under seprtion region is employed. Insted of computing the verge seprtor nd width, we tke the verge gp etween the seprtor points. Therefore, the time complexity is. 4.3 Finding the over seprtion As descried in Section 3.3, the resons for over seprtion re disjoint chrcter composition nd perceivly higher concve chrcter structure. The over seprtor points re detected sed on the frequent ppernce of the seprtor points thn expected. This could e seen in Fig 11, where the seprtor line 6 is closely locted to the seprtor 7. The lgorithm scns the seprtor points twice nd so the overll time complexity is. 4.4 Cretion of virtul column t the right end To work on the right mrgin of the document imge, we consider the lst non-zero entry of every row of RLE dt nd we uild virtul column. This is explined clerly in Section 3.1. The lgorithmic skeleton is provided here under. Algorithm VirtulColumn Input: RLE Output: VC Virtul Column- consists of lst non-zero vlue of every row in RLE dt This lgorithm tkes Fig. 12: Under seprtion nd frequency (Reference: A portion of ICDAR13 test imge tif) Algorithms in 4.1, 4.2 nd 4.3 cn e pplied on this virtul column to spot the seprtor points t right order of the document. A smple result of seprtor points t left nd right order is shown in Fig

7 The mchine lerning sttistics such s True Negtive (TN) nd Flse Positive (FP) in terms of under seprtion nd over seprtion respectively is shown elow. The totl seprtor points t left/right order of document is the sum of the numer of gps etween the text lines nd the two mrgins (top nd ottom) of the document pge. Fig. 13: Seprtor points t left nd right orders (Reference: A portion of ICDAR13 test imge tif) 5. EXPERIMENTAL ANALYSIS There is no stndrd compressed hndwritten dtset ville in the literture. However, the enchmrk dtsets such s ICDAR2013 [16], Alirez et l [17] of Knnd, Oriy, Persin nd Bngl documents re compressed using the RLE technique. The compression stndrd is dopted s presented in [14]. The system is evluted y counting the numer of mtches etween the entities (seprtor points) detected y the lgorithm nd the entities present in the ground truth, proposed in the literture [16]. Let N e the count of groundtruth elements nd the numer of one-to-one mtches e o2o, the detection rte (DR) is defined s follows: While experimenting, we ignore the seprtor point t the top mrgin of every document. The tle 3 shows the DR on evluting the lgorithms on the hndwritten dtsets. The tle shows one-to-one detection on oth ends (left nd right). Different threshold vlues including 1/15 nd 1/35 were experimented. However, the threshold vlue 1/25 would give reltively higher DR. In prticulr, the Persin hndwritten dtset holds lesser DR. This is ecuse the Persin chrcters or words re composed of disjoint components. For Persin texts the performnce t the right end is etter thn left ecuse it is written in left-to-right direction, cusing lrger indent mrgin t left end when compred to its right. Tle 3. Detection Rte tested with vrious compressed dtsets Dtsets (Hndwritten) Totl Lines (N) Detected Undetected (%) o2o Rte (%) Left Right Left Right Left Right TN FP TN FP ICDAR13 [16] Knnd [17] Oriy [17] Bngl [17] Persi [17] CONCLUSION AND FUTURE WORK In this pper, novel ide of working directly in the compressed representtion of the document imge is presented. We spotted the sequence of seprtor points t every line terminl in the RLE dt. These seprtor points would enle the text line segmenttion. Certinly, these points determine the text line segmenttion in the printed compressed document. Though the entire RLE dt is ville, we used just the first column of the RLE to spot seprtor points on the left end of the document. In cse of the right end, the lst non-zero entry of every row in the RLE dt is chosen to form virtul column. The lgorithm hs some limittions in working with skews, lrge mrgins (indents), consecutive touching lines nd disjoint chrcters. These limittions cn e considered for the future work. 7. REFERENCES [1] CCITT: 'Recommendtion T.6 Fcsimile Coding Schemes nd Coding Control Function from Group 4, Interntionl Telecommuniction Union', (Extrct from the Blue Book), Genev, [2] CCITT: 'Recommendtion T.4, Stndrdiztion of group 3 fcsimile pprtus for document trnsmission', terminl equipments nd protocols for telemtic services, vol. vii, fscicle, vii.3, genev, tech. rep.,

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