1 Iterative Multimodel Subimage Binarization for Handwritten Character Segmentation Author: Amer Dawoud and Mohamed S. Kamel Source: IEEE TRANSACTIONS.

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Iterative Multimodel Subimage Binarization for Handwritten Character Segmentation

Author: Amer Dawoud and Mohamed S. Kamel

Source: IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 13, NO. 9, SEPTEMBER 2004, pp. 1223-1230

Speaker: Ching-Hao Lai(賴璟皓 )

Date: 2004/10/13

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Outline Introduction

Iterative Multimodel Binarization Algorithm Feature Extraction

Setting Rejection Criteria

Selecting Subimages Optimal Thresholds

Experimental Results

Conclusion

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Introduction(1/3) Existing binarization methods:

Global binarization method Local binarization method

A document image is divided into subimages: Image(1) Image(2) … Image(M).

To find an optimal threshold for each subimage that would eliminate background noise.

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Introduction(2/3)

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Introduction(2/3)

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Introduction(3/3) The proposed method uses multimodels

to iteratively arrive at the optimal threshold for each subimage.

Based on gray-level and stroke-run

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ITERATIVE MULTIMODEL BINARIZATION ALGORITHM

The subimages are then binarized at a sequence of candidate thresholds CTi, where CT1 is the lowest possible threshold in gray-scale histogram.

The difference between two successive CTs was chosen to be eight gray-levels, which we found to be satisfactory.

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Feature Extraction When CTi failed to eliminate the background n

oise in Image (x). We want to infer such failure by comparing features of the binarized Image (x) with those of the other subimages.

Features: Gray-Level Features Stroke-run Features

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Gray-Level Feature

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Gray-Level Feature

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Stroke-Run Feature

Stroke-Run historgram: K={1,2,…,M},

4200 images, the longest run is 5 pixels

Unit-Run:

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Setting Rejection Criteria

Stroke-Width feature:

GRC: gray-level rejection criterion, SRC: stroke rejection criterion

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Flowchart

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Comparison result

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Comparison result

Comparison result

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Conclusions

When applied to a set of images that represent wide range of background complexity and noise levels, the multimodel algorithm succeeded in eliminating the background, and in preserving the handwritten characters.

As a result, higher recognition rate and lower substitution, insertion, and deletion error rates were achieved.