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Handwritten Word Recognition ( preprocessing ). CmpE 537 (Computer Vision) Aleksei Ustimov 2006800811. Preprocessing Tasks. Binarization Slant Correction Skeletonization Reference Lines Detection Segmentation. Data Collection. Writing any text using special pen,

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handwritten word recognition preprocessing

Handwritten Word Recognition (preprocessing)

CmpE 537 (Computer Vision)

Aleksei Ustimov

2006800811

preprocessing tasks
Preprocessing Tasks
  • Binarization
  • Slant Correction
  • Skeletonization
  • Reference Lines Detection
  • Segmentation
data collection
Data Collection
  • Writing any text using special pen,
  • Scanning written texts with 150 dpi resolution,
  • Separating isolated words with image processing tool.
binarization
Binarization
  • Otsu’s Thresholding method
    • Slow
    • Not sensible to details
  • Adaptive Thresholding method
    • Noisy
    • Requires tuning
slant correction
Slant Correction
  • Detects pen stroke width
  • Removes all lines with slant >60o
  • Removes small pieces
  • Calculates slant angle
  • Correct slant by shifting image rows
skeletonization
Skeletonization
  • Performs Holtz thinning until all lines are 1px wide
  • Removes small triangles using LYT removal algorithm
reference lines detection
Reference Lines Detection
  • Locates main body
  • Locates ascenders
  • Locates descenders
  • Calculates approximate position of reference lines
segmentation
Segmentation
  • Locates discontinuities in lines
  • Locates ligatures (character connection arcs)
  • Prefers oversegmentation
references
References
  • Seiler, R., Schenkel, M., Eggimann, F., Off-Line Cursive Handwriting Recognition Compared with On-Line Recognition, In Proc. IEEE-ICPR, Vol. 4, 1996, p. 505-509, 1996
  • Bunke, H., Roth, M., Schukat-Talamazzini, E.G., Off-line Cursive Handwriting Recognition Using Hidden Markov Models, Pattern Recognition, Vol. 28, No. 9, p. 1399-1413, 1995
  • Andrew, W., Robinson, A.J., An Off-Line Cursive Handwriting Recognition System, IEEE Transactions On Pattern Analysis and Machine Intelligence, Vol. 20, No. 3, p. 309-321, 1998
  • Wang, W., Brakensiek, A., Kosmala, A., Rigoll, G., HMM Based High Accuracy Off-Line Cursive Handwriting Recognition By A Baseline Detection Error Tolerant Feature Extraction Approach, In Proc. IWFHR-7, p. 209-218, 2000
  • Park, J., Govindaraju, V., Using Lexical Similarity In Handwritten Word Recognition, In Proc. IEEE Conference on Computer Vision and Pattern Recognition, Vol. 12, p. 290-295, 2000
  • Morita, M., Sabourin, R., Bortolozzi, F., Suen, C.Y., A Recognition and Verification Strategy For Handwritten Word Recognition, In Proc. ICDAR, Vol. 1, p. 482-486, 2003
  • Favata, J.T., Offline General Handwritten Word Recognition Using an Approximate BEAM Matching Algorithm, IEEE Tansactions on Pattern Analysis and Machine Intelligence, Vol. 23, No. 9, p. 1009-1021, 2001
  • Lavrenko, V., Rath, T., Manmatha, R., Holistic Word Recognition For Handwritten Historical Documents, In Proc. DIAL, p. 278-287, 2004
  • Liu, X., Shi, Z., A Format-Driven Handwritten Word Recognition System, ICDAR, Vol. 2, p. 1118-1122, 2003