| ---
|
| license: mit
|
| inference: true
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| language:
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| - en
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| metrics:
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| - cer
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| - wer
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| base_model:
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| - facebook/deit-base-patch16-224
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| - ai4bharat/IndicBART
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| pipeline_tag: image-to-text
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| tags:
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| - text-generation
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| - scene-text-recognition
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| - text-recognition
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| - computer-vision
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| - language-model
|
| ---
|
| # trocr-indic
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|
|
| This model utilizes the trocr approach to predict the **Indic Texts** from **cropped_images**.
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| ## Model Details
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|
|
| The model follows the TrOCR approach of training OCR for Scene Texts. Since, there is scarcity for generalized model for majority of Indian Languages, this model serves it replacement.
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|
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| 
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| *Courtesty: TrOCR - [original paper](https://huggingface.co/papers/2109.10282)*
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|
|
| The model is trained for the following languages:
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|
|
| - Assamese
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| - Bengali
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| - Gujarati
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| - Hindi
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| - Kannada
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| - Malayalam
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| - Marathi
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| - Odia
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| - Punjabi
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| - Telugu
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| - Tamil
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|
|
| ### Model Description
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|
|
| **IMPORTANT**
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| Although the model is trained on these languages due to limitations of IndicBART, the model is trained with only Devnagiri Scripts.
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|
|
| The output is in the following format:
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| ```
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| <LANGUAGE TOKEN> <TEXT TOKENS> <EOS TOKEN>
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| ```
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|
|
| The following flowchart gives a better picture on the approach of training and inference regarding this model.
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|
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| 
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| - **Datasets used:** [IndicSTR12](https://cvit.iiit.ac.in/research/projects/cvit-projects/indicstr)
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| - **Developed by:** Aarya Devarla
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| - **Model type:** Visio-Lingual Model / Vision-Language Model
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| - **License:** mit
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| - **Finetuned from model:** deit, indicBART
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|
|
| ### Results
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|
|
| | Metric | Assamese | Bengali | Gujarati | Hindi | Kannada | Malayalam | Marathi | Odia | Punjabi | Tamil | Telugu |
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| |--------|----------|---------|----------|-------|---------|-----------|---------|------|---------|-------|--------|
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| | CER | 0.069 | 0.133 | 0.058 | 0.075 | 0.212 | 0.154 | 0.082 | 0.120 | 0.097 | 0.122 | 0.220 |
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| | WER | 0.205 | 0.395 | 0.192 | 0.283 | 0.576 | 0.519 | 0.312 | 0.375 | 0.304 | 0.409 | 0.612 |
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|
|
| Well, the model isn't perfect. But it's a start.
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|
|
| ## Limitations
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|
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| The main limitation comes from IndicBART which is primarily trained on IndicTexts.
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|
|
| ### Recommendations
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|
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| Since the TrOCR is modular in approach one can just swap out the IndicBART model and train it with new model. Must keep in mind about the preprocessing and outputs.
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| |
