A unified method for augmented incremental recognition of online handwritten Japanese and English text

We present a unifed method to augmented incremental recognition for online handwritten Japanese and English text, which is used for busy or on-the-fly recognition while writing, and lazy or delayed recognition after writing, without incurring long waiting times. It extends the local context for segmentation and recognition to a range of recent strokes called "segmentation scope" and "recognition scop", respectively. The recognition scope is inside of the segmentation scope. The augmented incremental recognition triggers recognition at every several recent strokes, updates the segmentation and recognition candidate lattice, and searches over the lattice for the best result incrementally. It also incorporates three techniques. The frst is to reuse the segmentation and recognition candidate lattice in the previous recognition scope for the current recognition scope. The second is to fx undecided segmentation points if they are stable between character/word patterns. The third is to skip recognition of partial candidate character/word patterns. The augmented incremental method includes the case of triggering recognition at every new stroke with the above-mentioned techniques. Experiments conducted on TUAT-Kondate and IAM online database show its superiority to batch recognition (recognizing text at one time) and pure incremental recognition (recognizing text at every input stroke) in processing time, waiting time, and recognition accuracy

Grouping Text Lines in Online Handwritten Japanese Documents by Combining Temporal and Spatial Information

We present an effective approach for grouping text lines in online handwritten Japanese documents by combining temporal and spatial information. Initially, strokes are grouped into text line strings according to off-stroke distances. Each text line string is segmented into text lines by dynamic programming (DP) optimizing a cost function trained by the minimum classification error (MCE) method. Over-segmented text lines are then merged with a support vector machine (SVM) classifier for making merge/non-merge decisions, and last, a spatial merge module corrects the segmentation errors caused by delayed strokes. In experiments on the TUAT Kondate database, the proposed approach achieves the Entity Detection Metric (EDM) rate of 0.8816, the Edit-Distance Rate (EDR) of 0.1234, which demonstrates the superiority of our approach