Advice on fine tuning to improve accuracy on digits and special characters

[itsainii]

I'm trying to fine tune EasyOCR to improve accuracy with digits and special characters. I found this dataset on Kaggle and applied the method outlined in this tutorial.

I tested with 40 images just for testing, and here are the results I obtained:

exp_name: TPS-ResNet-BiLSTM-Attn-Seed1111 train_data: lmbd_archive2/ valid_data: lmbd_archive2/ manualSeed: 1111 workers: 0 batch_size: 1 num_iter: 10 valInterval: 5 saved_model: TPS-ResNet-BiLSTM-Attn.pth FT: False adam: False lr: 1 beta1: 0.9 rho: 0.95 eps: 1e-08 grad_clip: 5 baiduCTC: False select_data: ['/'] batch_ratio: ['0.5'] total_data_usage_ratio: 1.0 batch_max_length: 80 imgH: 32 imgW: 100 rgb: False character: 0123456789abcdefghijklmnopqrstuvwxyz sensitive: False PAD: False data_filtering_off: True Transformation: TPS FeatureExtraction: ResNet SequenceModeling: BiLSTM Prediction: Attn num_fiducial: 20 input_channel: 1 output_channel: 512 hidden_size: 256 num_gpu: 1 num_class: 38

[1/10] Train loss: 8.93992, Valid loss: 7.16805, Elapsed_time: 1.62669 Current_accuracy : 0.000, Current_norm_ED : 0.00 Best_accuracy : 0.000, Best_norm_ED : 0.00

Ground Truth | Prediction | Confidence Score & T/F

                      | sichasism                 | 0.0064	False

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[5/10] Train loss: 4.55050, Valid loss: 1.03087, Elapsed_time: 6.90535 Current_accuracy : 65.854, Current_norm_ED : 0.00 Best_accuracy : 65.854, Best_norm_ED : 0.00

Ground Truth | Prediction | Confidence Score & T/F

                      | iisile                    | 0.0647	False

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[10/10] Train loss: 0.01275, Valid loss: 0.22137, Elapsed_time: 10.19817 Current_accuracy : 95.122, Current_norm_ED : 0.00 Best_accuracy : 95.122, Best_norm_ED : 0.00

Ground Truth | Prediction | Confidence Score & T/F

                      |                           | 0.0000	True

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end the training

Any advice or suggestions on where I should go from here?