Name
Title | ||
|---|---|---|
SpottingNet: Learning the Similarity of Word Images with Convolutional Neural Network for Word Spotting in Handwritten Historical Documents |
Abstract | ||
|---|---|---|
Word spotting is a content-based retrieval process that obtains a ranked list of word image candidates similar to the query word in digital document images. In this paper, we present a convolutional neural network (CNN) based end-to-end approach for Query-by-Example (QBE) word spotting in handwritten historical documents. The presented models enable conjointly learning the representative word image descriptors and evaluating the similarity measure between word descriptors directly from the word image, which are the two crucial factors in this task. We propose a similarity score fusion method integrated with hybrid deep-learning classifica-tion and regression models to enhance word spotting perfor-mance. In addition, we present a sample generation method using location jitter to balance similar and dissimilar image pairs and enlarge the dataset. Experiments are conducted on the George Washington (GW) dataset without involving any recognition methods or prior word category information. Our experiments show that the proposed model yields a new state-of-the-art mean average precision (mAP) of 80.03%, significantly outperforming previous results. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/ICFHR.2016.0063 | 2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR) |
Keywords | Field | DocType |
word spotting,similarity learning,similarity score fusion,convolutional neural network | Similarity learning,Similarity measure,Convolutional neural network,Computer science,Digital document,Natural language processing,Artificial intelligence,Jitter,Artificial neural network,Spotting,Ranking,Pattern recognition,Speech recognition,Machine learning | Conference |
ISSN | ISBN | Citations |
2167-6445 | 978-1-5090-0982-4 | 0 |
PageRank | References | Authors |
0.34 | 12 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhuoyao Zhong | 1 | 58 | 4.21 |
| Weishen Pan | 2 | 11 | 3.31 |
| Lianwen Jin | 3 | 1337 | 113.14 |
| Harold Mouchère | 4 | 107 | 14.46 |
| Christian Viard-Gaudin | 5 | 444 | 46.20 |