A Segmentation Method of Single- and Multiple-Touching Characters in Offline Handwritten Japanese Text Recognition

Abstract

SUMMARYThis paper presents a method to segment single-and multiple-touching characters in offline handwritten Japanese text recognition with practical speed. Distortions due to handwriting and a mix of complex Chinese characters with simple phonetic and alphanumeric characters leave optical handwritten text recognition (OHTR) for Japanese still far from perfection. Segmentation of characters, which touch neighbors on multiple points, is a serious unsolved problem. Therefore, we propose a method to segment them which is made in two steps: coarse segmentation and fine segmentation. The coarse segmentation employs vertical projection, stroke-width estimation while the fine segmentation takes a graphbased approach for thinned text images, which employs a new bridge finding process and Voronoi diagrams with two improvements. Unlike previous methods, it locates character centers and seeks segmentation candidates between them. It draws vertical lines explicitly at estimated character centers in order to prevent vertically unconnected components from being left behind in the bridge finding. Multiple candidates of separation are produced by removing touching points combinatorially. SVM is applied to discard improbable segmentation boundaries. Then, ambiguities are finally solved by the text recognition employing linguistic context and geometric context to recognize segmented characters. The results of our experiments show that the proposed method can segment not only single-touching characters but also multiple-touching characters, and each component in our proposed method contributes to the improvement of segmentation and recognition rates.

Hiragana and Katakana Minutiae based Recognition System

“…On the basis of an in-house database containing approximately 50 samples for each of the 96 hiragana and katakana characters, experiments have been conducted to test the ability of the SVM (Support Vector Machine) to recognise the characters [15].…”

Recognition of Anomalously Deformed Kana Sequences in Japanese Historical Documents

“…(1) Vertical text line segmentation and concatenation method For vertical text line segmentation, we employ the segmentation method [13] tuned to vertical writing. Since there are many noises after binarization in historical documents, we remove connected components (CCs) that have areas smaller than the threshold of 25 pixels (5 × 5).…”

“…For images having widths equal or larger than AS, we employ our implementation [13] of the X-Y cut method [33] to separate them into text line images. The X-Y cut method calculates the vertical projection profile for each image and generates text-line borders at the transiting positions of nonzero projection to zero projection and zero projection to non-zero projection.…”

“…Next, we will briefly survey handwritten document recognition. Most traditional methods are based on presegmentation of a text line image into characters, especially for Japanese and Chinese [13], [14]. However, this presegmentation is costly and error prone because segmentation directly affects the performance of the whole system.…”