From: An analytical study of information extraction from unstructured and multidimensional big data
 | Purpose | Technique | Dataset | Results | Limitations/benefits |
---|---|---|---|---|---|
[51] | To possess high learning capacity To handle high dimensional data | CNN based OCR | Scanned Sanskrit document images (11,230) | Proposed approach outperform than existing. Accuracy was 93.32% | Training time as 1Â h with GPU |
[52] | To automatic recognition of handwritten text from images | CNN based OCR | MNIST | 98.11% accuracy rate | DL should apply on large datasets |
[53] | To compare the results of proposed DBN and CNN ECR | Unsupervised feature learning with DBN | HACDB dataset containing 6600 images | Experiments shown 3.64% and 14.71% for DBN and CNN resp. | DBN with unsupervised feature learning outperform CNN for high dimensional data |
[54] | To develop end to end mechanism for Scene TR | FANet using resnet as encoder and seq2seq attention mechanism as decoder | 5000 authentic seal dataset, 3660 real time train ticket dataset | Although, proposed approach could not achieve outperforming results but angular and horizontal TR was improved | Full attention mechanism was proposed to replace detect, slice, and recognize process with end to end recognition Ineffective for long text recognition |
[55] | To recognize text from handwritten and printed text images | TMIXT: tessetact for machine printed text recognition and LSTM for handwritten text recognition | IAM handwriting database | Achieved 80% average transcription accuracy | Heavy preprocessing is required for combined text recognition with proposed solution |
[56] | To recognize text using attention mechanism | CAN (Convolutional Attention Network), 2D CNN as encoder and one dimensional CNN decoder | Street View text SVT, IIIT5K, and ICDAR 03, ICDAR 13 dataset | The proposed model performed better than others on SVT and ICDAR 03 datasets | Improvement in proposed method is required for promising results |
[57] | Semantic based text recognition to extract useful information from images | CNN and bidirectional LSTM where convolutional part uses VGG and recurrent part uses bidirectional LSTM | Interior Design Dataset with 7708 images | Achieved 90% accuracy in word recognition | Generality improved but the text recognition from protest images is relatively an easy task. Evaluation of the system with complex and diverse datasets should be promising |