In this paper we have presented a novel segmentation technique for the implementation of an OCR (Optical Character Recognition) for printed Nastalique text, a calligraphic style of Urdu which uses the Arabic script for its writing.OCR for many of the world major languages have been developed and are being used but at present an OCR for Nastalique is not available and the published research on Nastalique OCR, Urdu OCR or even on any area of Urdu computing is almost non-existent, the reason being the challenges that the Nastalique style poses for

its optical recognition. We used Matlab 7 for our

experimentation the results are reported in this paper which are very encouraging

Haque, Mahmood K.Haque Pathan

Haque, Shamsul

Sattar, Sohail A.

UUM Repository

 Segmentation of Nastaliq Script for OCR Sohail A. Sattar, Shamsul Haque, Mahmood K. Pathan Department of Computer Science and Information TechnologyNED Universiy of Enginnering and Technology, Karachi, PakistanTel : 92-21-9261261, Fax : 92-21-9261255E-mail :{ sattar, pvc ,deanish}@neduet,edu.pk ABSTRACTIn this paper we have presented a novel segmentation technique  for  the  implementation  of  an  OCR  (Optical  Character Recognition) for printed Nastalique text, a calligraphic style of  Urdu which uses the Arabic script for its writing. OCR for many  of  the  world  major  languages  have  been  developed  and  are  being used but at present an OCR for Nastalique is not available and the published research on Nastalique OCR, Urdu OCR or  even on any area of Urdu computing is almost non-existent, the reason being the challenges that the Nastalique style poses for its  optical  recognition.  We  used  Matlab  7  for  our  experimentation the results are reported in this paper which are  very encouraging. KeywordsOCR, segmentation, Arabic script, histogram1.0 INTRODUCTIONA single script  with its  basic  character  shapes  is adapted  for writing in multiple languages e.g. the Latin script for English, German,  French  etc.  the  Arabic  script  has  been  adapted  for writing  Persian,  Urdu,  Pashtu,  Malay etc.  Arabic  writing  has many forms and styles, the more common being Naskh, while its calligraphic counterpart is Nastalique, the most popular style of  writing  Urdu,  the national  language of  Pakistan.  Beautiful and  decorative  as  it  is,  Nastalique  is  also  highly  cursive  by nature.The Urdu alphabet today contains 39 basic characters compared to  Roman  script  languages  which  have  a  fewer  number  of characters in their alphabet. For this reason the development of an  Urdu  OCR  is  fairly  more  difficult  than  Roman  script languages.  So  far  no  work  has  been  done  with  regard  to developing an Urdu OCR (Pal & Anirban, 2003) Each of the Urdu characters has 2-4 different shapes according to their position in the word: isolated, initial, medial and final. Many character shapes have multiple instances. The shapes are context sensitive too – character shapes changing with changes in the antecedent character or the precedent one. At times even the 3rd or 4th character may cause a similar change depicting an n-gram model in a Markov chain (Sattar, Hyder & Pathan, 2006).  Optical  Character  Recognition  or  OCR  is  the  text recognition system that allows hard copies of written or printed text  to  be  rendered  into  editable,  soft  copy  versions.  The problem of character  recognition is the problem of automatic recognition  of  raster  images  as  being  letters,  digits  or  some other  symbol  and  it  is  like  any  other  problem  in  computer vision (Parker, 1997).2.0 LITERATURE SURVEYA layout analysis for Urdu document  image understanding  is presented (Faisal, Adnan, Daniel & Thomas, 2006) highlighting its feature of right to left reading and writing order in contrast with Latin script  languages that  function from left to right. It considers  the  system  of  layout  analysis  as  an  important component of an OCR. The authors have experimented with a method  of  extracting  text  lines  for  image  processing  in  the reading  order  of  the  Urdu  script  which  is  presented  as  an essential  consideration  for  Urdu  document  image understanding.A  method  is  proposed  for  recognizing  isolated  characters claiming  98.3%  accuracy  for  printed  Urdu  alphabet  (Inam, Zaheer,  Jehanzeb & Awais,  2007).  However,  the system also claims to be script independent and yet it is designed for Urdu only.  The objective  of  the research is to develop an efficient recognition  system  for  Urdu  characters  using  minimum processing time.A  complete  scheme  for  character  recognition  of  totally unconstrained  Arabic  text  based  on  a  Model  Discriminant HMM is presented  (Alma’adeed,  Higgens  & Elliman,  2002). The system proposes feature extraction following the removal of  variations  in  the  word  images  which  do  not  affect  the identity  of  the  written  word.  The  system  then  encodes  the skeleton  and  edges  of  the  word  and  a  classification  process based on the HMM is used. The result is a word matching one in a dictionary. The study gives indication of successful results of a detailed experiment. A word-level  Arabic  text  recognition  system is implemented (Erlandson,  Trenkle  &  Vogt,  1996)  that  did  not  require character segmentation. They characterized the shape of Arabic words  by unique  feature  vectors.  These  feature  vectors  were then matched against a database of feature vectors derived from a  dictionary  of  known  words.  The  database  stored  multiple feature vectors for each word in a dictionary of 48,200 words. The word whose feature vectors strongly matched was returned as the hypothesis.An  Arabic  word  recognition  system  is  designed  and implemented  (Albadr  &  Haralick,  1998)  that  recognized  an input word by detecting a set of shape primitives on the word. The  regions  of  words  represented  by  these  shape  primitives were  then  matched  with  a  set  of  symbol  models.  The description of the recognized word was obtained from a spatial arrangement of symbol models that were matched to regions of the word.A  holistic  approach  for  Arabic  word  recognition  was introduced  (Khorsheed  &  Clocksin,  2000)  which  uses  a normalization  process  to  compensate  for  dilation  and translation.  The  process  adapted  transforms  the  image  of  an Arabic  word  from Cartesian  coordinates  to  polar  coordinates similar to log polar transformation. Rotation is also converted into translation by this transformation. A  method  was  proposed  (Bouslama  &  Kishibe,  1999)  that combined  the  structural  and  statistical  approach  for  feature extraction and a classification technique based on fuzzy logic. They  segmented  characters  into  a  main  and  complement characters. The main segment was then centered and projected horizontally and vertically. The features of classification were then extracted from the number of complement characters and from the horizontal and vertical projection profiles of the main character. A set of fuzzy rules was used for classification. The recognition  algorithm was tested on three  different  fonts  and high recognition rates were achieved.A structural probabilistic approach is presented (Amin & Mari, 1989) to recognize Arabic printed text. The system is based on character  recognition  and  word  recognition.  Character recognition  includes  segmentation  of  words  into  characters using vertical projections and identification of characters. Word recognition is based on Viterbi algorithm and can handle some identification errors. The system was tested on just a few words and  no  figures  were  reported  about  its  performance.  The method  has  inherent  ambiguity  and  deficiencies  due  to interconnectivity of Arabic text.3.0 NASTALIQ SCRIPT CHARACTERSTICSNastalique script has the following characteristics:• Text is written from right to left.• Numbers are written from left to right.• Urdu Nastalique script is inherently cursive in nature• A ligature is formed by joining two or more characters cursively in a free flow form.• A ligature is not necessarily a complete word, rather in most of the cases a part of a word, sometimes referred to as a sub-word.• A word  in  Nastalique  is  composed  of  ligatures  and isolated characters.• Word  forming  in Nastalique  is  context  sensitive  i.e. characters in a ligature change shape depending upon their position and preceding or succeeding characters.4.0 NASTALIQ SCRIPT SEGMENTATIONTo segment a text region in an image into lines of text we use the  horizontal  projection  profile  (or  histogram)  of  the  text image.Considering the image of size m× n is F (i, j), the projection of all  foreground  pixels  perpendicular  to  the  vertical  axis  and along the horizontal direction can be given as: ( ) ( )∑==njjiFiP1h ,  for      mi ≤≤1The horizontal projection profile of the text image separates the lines of text on the presence of white pixels between the two adjacent  lines.  A line of text covers  the foreground pixels on the  vertical  scan  from  top  of  ascenders  to  the  end  of  the descenders  in the line of text,  while scanning  vertically from top to bottom.One  of  the  techniques  for  segmenting  a text  line  image  into ligatures  and  isolated  characters  is  the  vertical  projection profile (or histogram) of the text line image, the projection of all  foreground  pixels  in the  image  of  extracted  line  L (i,  j), having size r× s, perpendicular to the horizontal axis and along the vertical direction can be given as:( ) ( )∑==riv jiLjP1,         for  sj ≤≤1The ligatures and the isolated characters present in the text line image are identified on the presence of white pixels separating them. The limitation of this technique is that it does not work in situations  where  adjacent  ligatures  overlap  or  shadow  each other and the histogram so obtained portrays them as a single ligature.5.0 EXPERIMENTATION AND RESULTSBefore  the  text  image  is  given  to  the  Nastaliq  script segmentation  system  first  it  is  binarized  then  the  system segments the text image into lines of text and each of the lines of  text  is  further  segmented  into  ligatures  and  isolated characters.Figure 1: Segmentation of text into linesFigure  1  illustrates  the  separation  of  lines  in  the  text  image using horizontal  projection profile or  histogram;  it also gives the correspondence between horizontal histograms and lines in the text image.               Figure 2: Seperation of ligatures and isolated charactersWe used connected component  labeling in Matlab to identify and  isolate  all  the  ligatures  in  the  text  image,  illustrated  in figure  2.  This  way  we  are  able  to  isolate  all  ligatures  and isolated characters. However, as an undesired effect all diacritic marks  are  also  separated  like  dots  belonging  to  certain characters.  When a character  has  dots,  this is  identified with dots otherwise this will be interpreted as some other character. In Urdu we have different characters with the same base shape but  having  different  numbers  of  dots,  or  dots  at  different locations or having no dots at all. The dots are to be associated with  the  character  shape  so that  it  can  be  interpreted  as  the correct character shape.      Figure 3: Analysis of text line 1After segmenting a text image into separate lines, each of the text line images is assigned its number and processed separately further to identify the number of ligatures, isolated characters and diacritical marks it contains.The given text  image consists  of seven lines (Figure 1).  The text image is first segmented into seven lines and then each of the  lines  is  further  processed  and  the  results  are  shown  in figures 2 and 3.The segmentation phase is shown in Figure 1, where the text image is first segmented into lines, and then each of the lines into ligatures, isolated characters and primitives. Here we call diacritic marks as primitives.Figure  3 shows that  it  has  22  elements  out  of  which  10  are diacritical marks and rest of the 12 are ligatures. The distinction between a ligature and a diacritical mark is made by trial and error method on counting the number of black pixels in each of the  bounding  box  when  connected  component  labeling  is applied on separated text line images. By experimentation we found that if the number of black pixels in a bounding box is more than 25 then it is a ligature otherwise a diacritical mark.In case of the Latin script or other discrete script OCRs we can use  vertical  projection  profile  (or  histogram)  to  separate  all characters in a line of text on the basis of small white spaces present between characters and their non-overlapping nature.In  figure  4 vertical  projection  profile  is  used  on the  vertical axis. Pixel density is plotted along horizontal axis (position on the text line). However, some ligatures could not be separated for  recognition  due  to  the  overlapping  between  adjacent ligatures.Once the text line images are separated from the text image and are processed as separate  text  image having  only one  line of text in the image.Figure 4: Ligature overlap line 1In  discrete  scripts  like  Roman  script  for  example  individual characters are separated by the vertical projection profile of the text image with projecting all black pixels in the text line image along  the  vertical  axis  leaving  white  gaps  in  between  the characters.  In  printed  Roman  text  discrete  characters  do  not overlap or shadow each other.However in the case of Nastalique script, due to its cursiveness there is very much overlapping and shadowing of ligatures in the  printed  text,  so  ligatures  cannot  be  separated  by  using  a simple vertical histogram of text line image.Figure 4 illustrate it clearly that there is much overlapping of words and ligatures in the printed Nastalique text and ligature separation is a more challenging job.The text line image is scanned horizontally form left to right looking for the black (foreground) pixels and as they are found these are projected along the vertical axis to give the histogram showing distribution of pixel along the horizontal axis and the pixel densities along  the vertical axis. 6.0 FUTURE WORKIn  future  we  plan  to  use  our  Nastaliq  script  segmentation system  to  extend  to  a  Nastaliq  OCR  system  by  training  a learning  machine  to  recognize  the  ligatures  and  isolated characters,  like  Neural  Networks  (NN),  Support  Vector Machines (SVM) or Hidden Markov Models (HMM).REFERENCESAlbadr, B., Haralick, R. (1998). Segmentation-free approach to text  recognition  with  application  to  Arabic  text. International  Journal  on  Document  Analysis  and Recognition, Vol. 1: 147-166.Alma’adeed,  S.,  Higgens,  C.  &  Elliman,  D.  (2002). Recognition  of  Off-Line  Handwritten  Arabic  Words Using Hidden Markov Model Approach,  Proc. 16th Int’l  Conf. Pattern Recognition, vol. 3, 481-484.Amin, A., Mari, J. (1989). Machine recognition and correction of  printed  Arabic  text.  IEEE  Transactions  on  Man,  Machine and Cybernetics, 19(5): 1300-1306.Bouslama,  F.,  Kishibe,  H.  (1999).  Fuzzy  logic  in  the recognition of printed Arabic text.  IEEE Transactions on Pattern Recognition, 1150-1154.Erlandson,  E.,  Trenkle,  J.,  Vogt,  R.  (1996).  Word  level recognition of multifont Arabic text using a feature vector matching  approach,  Proceedings of International Society  for Optical Engineers, SPIE,  2660: 63-70.Faisal, S., Adnan, H., Daniel, K., & Thomas, M. (2006). Layout Analysis of Urdu Document Images.  Proceedings of 10th IEEE  International  Multitopic  Conference,  WMIC  ’06,  Islamaad, Pakistan.Inam, S., Zaheer, A., Jehanzeb, K. & Awais, A. (2007).  OCR For  Printed  Urdu  Script  Using  Feed  Forward  Neural Network,  Proceedings  of  World  Academy  of  Science,  Engineering  and  Technology,  volume  23, ISSN 1307-6884.Khorsheed,  S.,  Clocksin,  W.  (2000).  Spectral  features  for Arabic  word  recognition.  The  IEEE  International  Conference on Acoustics,  Speech, and Signal Processing ICASSP, Istanbul, Turkey, 3574-3577.Pal, U. and Anirban, S. (2003).  Recognition for Printed Urdu Script,  Proceedings  of  the  Seventh  International  Conference on Document Analysis and Recognition.Parker  JR.  (1997).  Algorithms  For  Image  Processing  and Computer Vision, John Wiley & Sons.Sattar, S. A., Hyder, S. S. & Pathan, M. K. (2006). Problems of Nastalique OCR: A comparison of Nastalique and Roman script  OCRs,  Proceedings  of  the  ICCCE 06,  Kuala Lumpur, Malaysia, Vol. 2, 1066-1071.

Segmentation of Nastaliq script for OCR

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Segmentation of Nastaliq script for OCR

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