Name
Abstract | ||
|---|---|---|
The presence of struck-out texts in handwritten manuscripts adversely affects the performance of state-of-the-art automatic handwritten document processing systems. The information of struck-out words (STW) are often important for real-time applications like handwritten character recognition, writer identification, digital transcription, forensic applications, historical document analysis etc. Hence, the detection of STW and localisation of struck-out strokes (SS) are crucial tasks. In this paper, we introduce a system for simultaneous detection of STWs and localisation of the SS using a single network architecture based on Generative Adversarial Network (GAN). The system requires no prior information about the type of SS stroke and it is also able to robustly handle variant of strokes like straight, slanted, cris-cross, multiple-lines, underlines and partial STW as well. However, we also present a methodology to generate STW with high variability of SS for network learning. We have evaluated the proposed pipeline on publicly available IAM dataset and also on struck-out words collected from real-world writers with high variability factors like age, gender, stroke-width, stroke-type etc. The evaluation metrics show robustness and applicability in real-world scenario. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/978-3-030-86159-9_7 | DOCUMENT ANALYSIS AND RECOGNITION, ICDAR 2021, PT II |
Keywords | DocType | Volume |
Handwritten document image, Struck-out words, Handwritten manuscripts, Generative Adversarial Networks (GAN) | Conference | 12917 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Arnab Poddar | 1 | 6 | 2.13 |
| Akash Chakraborty | 2 | 0 | 0.34 |
| Jayanta Mukhopadhyay | 3 | 72 | 26.05 |
| Prabir Kumar Biswas | 4 | 410 | 39.88 |