Joint Line Segmentation and Transcription for End-to-End Handwritten Paragraph Recognition
Authors: Theodore Bluche
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We carried out experiments on two public datasets of handwritten paragraphs: Rimes and IAM. |
| Researcher Affiliation | Industry | Théodore Bluche A2i A SAS 39 rue de la Bienfaisance 75008 Paris tb@a2ia.com |
| Pseudocode | No | No pseudocode or clearly labeled algorithm blocks were found in the paper. |
| Open Source Code | No | The paper does not provide an explicit statement about open-sourcing the code or a link to a code repository. |
| Open Datasets | Yes | We carried out the experiments on two public databases. The IAM database [29] is made of handwritten English texts copied from the LOB corpus. The Rimes database [1] contains handwritten letters in French. |
| Dataset Splits | Yes | The IAM database [29] is made of handwritten English texts copied from the LOB corpus. There are 747 documents (6,482 lines) in the training set, 116 documents (976 lines) in the validation set and 336 documents (2,915 lines) in the test set. The Rimes database [1] contains handwritten letters in French. The data consist of a training set of 1,500 paragraphs (11,333 lines), and a test set of 100 paragraphs (778 lines). We held out the last 100 paragraphs of the training set as a validation set. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | The networks are trained with RMSProp [36] with a base learning rate of 0.001 and mini-batches of 8 examples, to minimize the CTC loss over entire paragraphs. |