An Approach to Recognize Bangla Digits from Digital Image

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1 248 IJCSNS Internatinal Jurnal f Cmputer Science and Netwrk Security, VOL.11 N.6, June 2011 An Apprach t Recgnize Bangla Digits frm Digital Image Abdul Kadar Muhammad Masum 1, Mhammad Shahjalal 2, Md. Iqbal Hasan Sarker 3, Md. Faisal Faruque 4 1 Assistant Prfessr, 2,3,4 Lecturer 1.2 Internatinal Islamic University Chittagng, Bangladesh, 3 Chittagng University f Engineering & Technlgy 4 University f Infrmatin Technlgy & Sciences Summary Pattern recgnitin is imprtant in cnverting digital dcument image int electrnic dcument. Tday there are sme OCR sftwares t read the dcuments frm scanned papers. We have designed a prcess t recgnize the Bangla digits frm digital images. Bangla digits are discrete when they are used in dcuments t write numbers. Each character has its wn shape and design. We have used these characteristics f Bangla digits t recgnize them frm digital images. We have applied different mrphlgical peratin and segmentatin techniques t separate each digit r character frm the image. Then we have just drawn a single bit utline f the character, and generated a sequence based n the traversing path f the brder utline. Then we applied a lngest cmmn subsequence peratin between the generated sequence f the character and the pre-defined sequence f the 10 digits and detected the unknwn Bangla digit. This is a sequence-based methd. Key wrds: Digital image, Image prcessing, Bangla digit, LCS (Lngest Cmmn Subsequence), DFS (Depth First Search). 1. Intrductin The cnversin f paper-based dcument infrmatin t electrnic frmat is imprtant fr autmated dcument delivery, jurnal distributin, dcument preservatin, and ther dcument related applicatins. In bimedical dcuments, especially dcuments in mlecular bilgy, bichemistry, pharmaclgy, drug develpment, chemical analysis, etc. We have develped an idea t recgnize a Bengali digit frm an image f dcument. Our prcess generates a special sequence n the shape f the characters r bjects. And analyzing the sequence we can decide that which digit it is. 2. Literature review 2.1. Pattern Recgnitin Pattern recgnitin is a sub-tpic f machine learning. It can be defined as "the act f taking in raw data and taking an actin based n the categry f the data" [1]. Mst research in pattern recgnitin is abut methds fr supervised learning and unsupervised learning. Pattern recgnitin aims t classify data (patterns) based n either a priri knwledge r n statistical infrmatin extracted frm the patterns. The patterns t be classified are usually grups f measurements r bservatins, defining pints in an apprpriate multidimensinal space. A cmplete pattern recgnitin system cnsists f a sensr that gathers the bservatins t be classified r described; a feature extractin mechanism that cmputes numeric r symblic infrmatin frm the bservatins; and a classificatin r descriptin scheme that des the actual jb f classifying r describing bservatins, relying n the extracted features. The classificatin r descriptin scheme is usually based n the availability f a set f patterns that have already been classified r described. This set f patterns is termed the training set and the resulting learning strategy is characterized as supervised learning. Learning can als be unsupervised, in the sense that the system is nt given an a priri labeling f patterns, instead it establishes the classes itself based n the statistical regularities f the patterns. The classificatin r descriptin scheme usually uses ne f the fllwing appraches: statistical (r decisin theretic), syntactic (r structural). Statistical pattern recgnitin is based n statistical characterizatins f patterns, assuming that the patterns are generated by a prbabilistic system. Structural pattern recgnitin is based n the structural interrelatinships f features. A wide range f algrithms can be applied fr pattern recgnitin, frm very simple Bayesian classifiers t much mre pwerful neural netwrks.[2] An intriguing prblem in pattern recgnitin yet t be slved is the relatinship between the prblem t be slved (data t be classified) and the perfrmance f varius pattern recgnitin algrithms (classifiers). Van Dar Walt and Barnard investigated very specific artificial data sets t determine cnditins under which certain classifiers perfrm better and wrse than ther. Hlgraphic assciative memry is anther type f pattern matching scheme where a target small patterns can be searched frm a large set f learned patterns based n cgnitive meta-weight. Manuscript received June 5, 2011 Manuscript revised June 20, 2011

2 IJCSNS Internatinal Jurnal f Cmputer Science and Netwrk Security, VOL.11 N.6, June Typical applicatins are autmatic speech recgnitin, classificatin f text int several categries (e.g. spam/nn-spam messages), the autmatic recgnitin f handwritten pstal cdes n pstal envelpes, r the autmatic recgnitin f images f human faces. The last tw examples frm the subtpic image analysis f pattern recgnitin that deals with digital images as input t pattern recgnitin systems. Pattern recgnitin is mre cmplex when templates are used t generate variants. Fr example, in English, sentences ften fllw the "N-VP" (nun - verb phrase) pattern, but sme knwledge f the English language is required t detect the pattern. Pattern recgnitin is studied in many fields, including psychlgy, ethlgy, and cmputer science. Decisin 3. Prpsed Apprach f Bangla Digits Recgnitin We have designed ur apprach fr recgnizing Bangla digits in the fllwing steps 3.1. Prpsed Mdel f Bangla Digit Recgnitin Classificatin Pst Prcessing Classificatin Feature Extractin Segmentatin Input Fig. 1: Pattern recgnitin system Template Matching Feature Extractin Segmentatin Decisin Template matching is defined as the ideal representatin f the bject (r pattern) t be identified r classified within the image. The maximum crrelatin matrix value dentes where the template match with the image much prperly than ther. Mving the template t every psitin in the image and evaluating the degree f similarity at each crrelatin r psitin. There are many disadvantages that assciate with the template matching they are as fllws: Presence f the shadws in the images Nise in analg r gray scale images Sampling and quantizatin nise Imprper illuminatins in the images Als, the template matching is heavily relies n backgrund illuminatin and discriminatin which are the mst varying factrs in the images hence it is nt a feasible ptin t g fr. [3] Fig.2 Pictrial diagram f prpsed algrithm 3.2. Prpsed Algrithm Our ttal wrk can be separated int fllwing parts. 1. Input an Image Brdering 4. Sequence Generatin 5. Making Decisin 3.2.1Input Image Classificatin Feature Extractin Input image may be any digital scanned image, which cntains Bangla numeric digits.

250 IJCSNS Internatinal Jurnal f Cmputer Science and Netwrk Security, VOL.11 N.6, June 2011 3.2.2 Image Preprcessing Fig3: Input Image Nise Reductin: We have applied mean filtering and high pass filtering in ur prcess t reduce the nises f the image.

3 250 IJCSNS Internatinal Jurnal f Cmputer Science and Netwrk Security, VOL.11 N.6, June Image Preprcessing Fig3: Input Image Nise Reductin: We have applied mean filtering and high pass filtering in ur prcess t reduce the nises f the image. [4] Fig.7: Resized character Fig.4: Nise Reduced Binarizatin: After reducing the nises frm the image apply threshld peratin t cnvert it int binary image. Padding: Nw the resized Digit will be padded. That is there will be an extra brder added t the image. S the image will be increased by the size f tw rws and tw clumns. This is necessary t avid the bundary f the image while checking the eight neighbrs f each pixel Brdering Fig.5: Binary image f the input Segmentatin: In this step we have segment this image int numbers and digits. The image will be segmented int hrizntal lines. Then each line is an individual image. Again each line will be segmented int numbers r integers depending n the spaces between the digits. Nw the numbers are segmented. The digits will be separated frm the numbers. And further peratins will be dne n the digits. In this stage a single pixel brder f the digit will be generated. T generate single pixel brder we have run mrphlgical edge detectin peratin by using Sbel [5] peratr. The fund brder is several pixel thick. Then we applied the iterative thinning peratin till ne pixel brder is nt fund. As each digit has different shape s, the brders will als be f different shape. And this brder must be a cnnected graph. Fig.8: Brdeing Fig. 6: Character segment Resizing: This prcess is designed fr specific size f fnts. S if a large fnt is given then this fnt must be resized int specific size. Again if this image is small then this shuld be resized in large size Sequence Generatin A sequence will be generated n the basis f the brder. T generate the sequence we have decided t run the Depth First Search (DFS) [6]. We always start frm the bttm left mst pint f the brder. It starts traverse in the left tp directin and stps in the previus pint f starting pint.

IJCSNS Internatinal Jurnal f Cmputer Science and Netwrk Security, VOL.11 N.6, June 2011 251 4. Perfrmance Evaluatin There are given achieved result n basis f testing with different sized fnts 4.1. Testing with different sized fnts 3.

4 IJCSNS Internatinal Jurnal f Cmputer Science and Netwrk Security, VOL.11 N.6, June Perfrmance Evaluatin There are given achieved result n basis f testing with different sized fnts 4.1. Testing with different sized fnts 3.2.4Making Decisin Fig.9: Sequencing Directin Fig.10: Generating sequence Nw it is ready t take decisin which digit it is. Steps are discussed belw Cmpare Sequence We have defined sme standard sequences fr the ten Bengali digits ( ). T defining the sequences we have generated three sequences fr large, medium and small fnt f each digit. T take decisin we preferred t run the lngest cmmn subsequence (LCS) [7] n the new generated sequence and the pre-defined sequences. We have tested ur implemented system with varius sized digits and fund the fllwing results. Size 9 N Y N Y Y N Y Y N N 10 Y Y N Y Y Y Y Y Y Y 15 Y Y Y Y Y Y Y Y Y Y 20 Y Y Y Y Y Y Y Y Y Y 40 Y Y Y Y Y Y Y Y Y Y 100 Y Y Y Y Y Y Y Y Y Y 200 Y Y N Y Y Y Y Y Y N 4.2 Result We have tested ur prgram with different Bangla digits f different fnt and size. It can successfully recgnize mst f the digits frm the image. It has sme limitatins thse are discussed here. 5. Advantages fr prpsed technique Here we dn t have used any Neural Netwrk. N need t train the prgram t recgnize the digits. Simple Algrithms. Easy t Implement. Fig.11: Pre-defined Sequences LCS gives the maximum subsequence that refines the rder f the sequence fr that special digit. S when LCS runs thrugh the pre-defined sequences it finds ut the cmmn sequence with all the predefined sequences. As LCS gives the length f subsequence s there is a matter f rder f sequence. S lngest subsequence matches with the sequence f that digit. S, after cmparing with all the sequences we can decide the digit based n the length f LCS. 6. Cnclusin Generally digit recgnitin techniques are used in ptical character recgnitin sftware. Usually character recgnitin techniques use Artificial Intelligence and Neural Netwrk t perfrming the recgnitin. Here we discussed a system which didn t use any Neural Netwrk and Artificial Intelligence. S, ur prpsed system will wrk faster with limited database, where Neural Netwrk takes a larger Database.

252 IJCSNS Internatinal Jurnal f Cmputer Science and Netwrk Security, VOL.11 N.6, June 2011 7. Future Wrks The technique we have described here can be applied t recgnize any discrete character.

It can be used in ptical character recgnitin fr Bangla & Arabic numbers and English texts and numbers als. Accuracy can be increased by taking different sequences frm different sized character.

Barnard, Data characteristics that determine classifier perfrmance, in Prceedings f the Sixteenth Annual Sympsium f the Pattern Recgnitin Assciatin f Suth Africa, pp.160-165, 2006.

[3] Wikipedia: http://en.wikipedia.rg/wiki/mrphlgical_image_prcessi ng [4] R. Gnzalez and R. Wds Digital Image Prcessing, Addisn-Wesley Publishing Cmpany, 1992 [5] http://www.cee.hw.ac.

5 252 IJCSNS Internatinal Jurnal f Cmputer Science and Netwrk Security, VOL.11 N.6, June Future Wrks The technique we have described here can be applied t recgnize any discrete character. Just we have t set a well defined sequence f the characters in the prgram. English alphabets, numerals and Arabic numerals are als discrete when they are written in dcuments. It can be used in ptical character recgnitin fr Bangla & Arabic numbers and English texts and numbers als. Accuracy can be increased by taking different sequences frm different sized character. Perfrmance can als be increased by ding the image pre-prcessing efficiently. References [1] C.M. van der Walt and E. Barnard, Data characteristics that determine classifier perfrmance, in Prceedings f the Sixteenth Annual Sympsium f the Pattern Recgnitin Assciatin f Suth Africa, pp , Available [Online] [2] Text line segmentatin and wrd recgnitin in a system fr general writer independent handwriting recgnitin Marti, U.-V.; Bunke, H. [3] Wikipedia: ng [4] R. Gnzalez and R. Wds Digital Image Prcessing, Addisn-Wesley Publishing Cmpany, 1992 [5] [6] ww3.algrithmdesign.net/handuts/dfs.pdf [7] LCS 161: Design and Analysis f Algrithms Lecture ntes fr February 29, 1996 Abdul Kadar Muhammad Masum is a graduate f B.Sc and MSc in Cmputer Science and Engineering frm Internatinal Islamic University Chittagng, and United Internatinal University, Dhaka, Bangladesh, respectively. He has been serving as a faculty member in IIUC since Nw he is an Assistant Prfessr f Department f Business Administratin, Internatinal Islamic University Chittagng, Bangladesh. Mrever, he has mentinable research wrks n Data Mining, E-cmmerce and E-gvernance and has a great interest in MIS field. He has als experience in research paper presentatin in many Internatinal and Natinal Cnferences. University Chittagng since February 28, His research interests fcus n Artificial Intelligence, image prcessing, wireless cmmunicatin, wireless netwrking, digital signal prcessing. Md. Iqbal Hasan Sarker has received his B.Sc degree in Cmputer Science & Engineering frm Chittagng University f Engineering & Technlgy (CUET) in Nw he is serving as a Lecturer in Chittagng University f Engineering & Technlgy (CUET) in the department f Cmputer Science & Engineering since September 19, His research interests fcus n Artificial Intelligence, Digital Image Prcessing and Natural Language Prcessing, Wireless Cmmunicatin. Md. Faisal Faruque has received his B.Sc degree in Cmputer Science & Engineering frm Internatinal Islamic University Chittagng (IIUC) in Nw he is serving as a Lecturer in University f Infrmatin Technlgy & Sciences (UITS) in the department f Cmputer Science & Engineering since February 18, His research interests fcus n Artificial Intelligence, Digital Image Prcessing and Natural Language Prcessing. Mhammad Shahjalal has received his B. Sc. degree in Electrical and Electrnic Engineering frm Chittagng University f Engineering and Technlgy in Nw he is serving as a lecturer in the department f Electrical and Electrnic Engineering f Internatinal Islamic

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