---
thumbnail: "url to a thumbnail used in social sharing"
tags:
- tag1
- tag2
license: apache-2.0
datasets:
- dataset1
- dataset2
metrics:
- metric1
- metric2
---

Model:OCR box to json 

Model Usage:
```
### Instruction:
You are POS receipt data expert, parse, detect, recognize and convert following receipt OCR image result into structure receipt data object. 
Don't make up value not in the Input. Output must be a well-formed JSON object.```json

### Input:
[[[[184.0, 42.0], [278.0, 45.0], [278.0, 62.0], [183.0, 59.0]], ('BAJA FRESH', 0.9551795721054077)], [[[242.0, 113.0], [379.0, 118.0], [378.0, 136.0], [242.0, 131.0]], ('GENERAL MANAGER:', 0.9462024569511414)], [[[240.0, 133.0], [300.0, 135.0], [300.0, 153.0], [240.0, 151.0]], ('NORMAN', 0.9913229942321777)], [[[143.0, 166.0], [234.0, 171.0], [233.0, 192.0], [142.0, 187.0]], ('176 Rosa C', 0.9229503870010376)], [[[130.0, 207.0], [206.0, 210.0], [205.0, 231.0], [129.0, 228.0]], ('Chk 7545', 0.9349349141120911)], [[[283.0, 215.0], [431.0, 221.0], [431.0, 239.0], [282.0, 233.0]], ("Dec26'0707:26PM", 0.9290117025375366)], [[[440.0, 221.0], [489.0, 221.0], [489.0, 239.0], [440.0, 239.0]], ('Gst0', 0.9164432883262634)], [[[164.0, 252.0], [308.0, 256.0], [308.0, 276.0], [164.0, 272.0]], ('TAKE OUT', 0.9367803335189819)], [[[145.0, 274.0], [256.0, 278.0], [255.0, 296.0], [144.0, 292.0]], ('1 BAJA STEAK', 0.9167789816856384)], [[[423.0, 282.0], [465.0, 282.0], [465.0, 304.0], [423.0, 304.0]], ('6.95', 0.9965073466300964)], [[[180.0, 296.0], [292.0, 299.0], [292.0, 319.0], [179.0, 316.0]], ('NO GUACAMOLE', 0.9631438255310059)], [[[179.0, 317.0], [319.0, 322.0], [318.0, 343.0], [178.0, 338.0]], ('ENCHILADO STYLE', 0.9704310894012451)], [[[423.0, 325.0], [467.0, 325.0], [467.0, 347.0], [423.0, 347.0]], ('1.49', 0.988395631313324)], [[[159.0, 339.0], [201.0, 341.0], [200.0, 360.0], [158.0, 358.0]], ('CASH', 0.9982023239135742)], [[[417.0, 348.0], [466.0, 348.0], [466.0, 367.0], [417.0, 367.0]], ('20.00', 0.9921982884407043)], [[[156.0, 380.0], [200.0, 382.0], [198.0, 404.0], [155.0, 402.0]], ('FOOD', 0.9906187057495117)], [[[426.0, 390.0], [468.0, 390.0], [468.0, 409.0], [426.0, 409.0]], ('8.44', 0.9963030219078064)], [[[154.0, 402.0], [190.0, 405.0], [188.0, 427.0], [152.0, 424.0]], ('TAX', 0.9963871836662292)], [[[427.0, 413.0], [468.0, 413.0], [468.0, 432.0], [427.0, 432.0]], ('0.61', 0.9934712648391724)], [[[153.0, 427.0], [224.0, 429.0], [224.0, 450.0], [153.0, 448.0]], ('PAYMENT', 0.9948703646659851)], [[[428.0, 436.0], [470.0, 436.0], [470.0, 455.0], [428.0, 455.0]], ('9.05', 0.9961490631103516)], [[[152.0, 450.0], [251.0, 453.0], [250.0, 475.0], [152.0, 472.0]], ('Change Due', 0.9556287527084351)], [[[420.0, 458.0], [471.0, 458.0], [471.0, 480.0], [420.0, 480.0]], ('10.95', 0.997236430644989)], [[[209.0, 498.0], [382.0, 503.0], [381.0, 524.0], [208.0, 519.0]], ('$2.000FF', 0.9757758378982544)], [[[169.0, 522.0], [422.0, 528.0], [421.0, 548.0], [169.0, 542.0]], ('NEXT PURCHASE', 0.962527871131897)], [[[167.0, 546.0], [365.0, 552.0], [365.0, 570.0], [167.0, 564.0]], ('CALL800 705 5754or', 0.926964521408081)], [[[146.0, 570.0], [416.0, 577.0], [415.0, 597.0], [146.0, 590.0]], ('Go www.mshare.net/bajafresh', 0.9759786128997803)], [[[147.0, 594.0], [356.0, 601.0], [356.0, 621.0], [146.0, 614.0]], ('Take our brief survey', 0.9390400648117065)], [[[143.0, 620.0], [410.0, 626.0], [409.0, 647.0], [143.0, 641.0]], ('When Prompted, Enter Store', 0.9385656118392944)], [[[142.0, 646.0], [408.0, 653.0], [407.0, 673.0], [142.0, 666.0]], ('Write down redemption code', 0.9536812901496887)], [[[141.0, 672.0], [409.0, 679.0], [408.0, 699.0], [141.0, 692.0]], ('Use this receipt as coupon', 0.9658807516098022)], [[[138.0, 697.0], [448.0, 701.0], [448.0, 725.0], [138.0, 721.0]], ('Discount on purchases of $5.00', 0.9624248743057251)], [[[139.0, 726.0], [466.0, 729.0], [466.0, 750.0], [139.0, 747.0]], ('or more,Offer expires in 30 day', 0.9263916611671448)], [[[137.0, 750.0], [459.0, 755.0], [459.0, 778.0], [137.0, 773.0]], ('Good at participating locations', 0.963909924030304)]]

### Output:
```
```json
{
  "receipt": {
    "store": "BAJA FRESH",
    "manager": "GENERAL MANAGER: NORMAN",
    "address": "176 Rosa C",
    "check": "Chk 7545",
    "date": "Dec26'0707:26PM",
    "tax": "Gst0",
    "total": "20.00",
    "payment": "CASH",
    "change": "0.61",
    "discount": "Discount on purchases of $5.00 or more,Offer expires in 30 day",
    "coupon": "Use this receipt as coupon",
    "survey": "Take our brief survey",
    "redemption": "Write down redemption code",
    "prompt": "When Prompted, Enter Store Write down redemption code Use this receipt as coupon",
    "items": [
      {
        "name": "1 BAJA STEAK",
        "price": "6.95",
        "modifiers": [
          "NO GUACAMOLE",
          "ENCHILADO STYLE"
        ]
      },
      {
        "name": "TAKE OUT",
        "price": "1.49"
      }
    ]
  }
}
```

# Load model directly
```python
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mychen76/mistral7b_ocr_to_json_v1")
model = AutoModelForCausalLM.from_pretrained("mychen76/mistral7b_ocr_to_json_v1")

prompt=f"""### Instruction:
You are POS receipt data expert, parse, detect, recognize and convert following receipt OCR image result into structure receipt data object. 
Don't make up value not in the Input. Output must be a well-formed JSON object.```json

### Input:
{receipt_boxes}

### Output:
"""
with torch.inference_mode():
    inputs = tokenizer(prompt,return_tensors="pt",truncation=True).to(device)
    outputs = model.generate(**inputs, max_new_tokens=512) 
    result_text = tokenizer.batch_decode(outputs)[0]
    print(result_text)
```

## Get OCR Image boxes
```python
from paddleocr import PaddleOCR, draw_ocr
from ast import literal_eval
import json

paddleocr = PaddleOCR(lang="en",ocr_version="PP-OCRv4",show_log = False,use_gpu=True)

def paddle_scan(paddleocr,img_path_or_nparray):
    result = paddleocr.ocr(img_path_or_nparray,cls=True)
    result = result[0]
    boxes = [line[0] for line in result]       #boundign box 
    txts = [line[1][0] for line in result]     #raw text
    scores = [line[1][1] for line in result]   # scores
    return  txts, result

# perform ocr scan
receipt_texts, receipt_boxes = paddle_scan(paddleocr,receipt_image_array)
print(50*"--","\ntext only:\n",receipt_texts)
print(50*"--","\nocr boxes:\n",receipt_boxes)

```

# Load model in 4bits
```python

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig, BitsAndBytesConfig

# quantization_config = BitsAndBytesConfig(llm_int8_enable_fp32_cpu_offload=True)
bnb_config = BitsAndBytesConfig(
    llm_int8_enable_fp32_cpu_offload=True,
    load_in_4bit=True,
    bnb_4bit_use_double_quant=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
)
# control model memory allocation between devices for low GPU resource (0,cpu)
device_map = {
    "transformer.word_embeddings": 0,
    "transformer.word_embeddings_layernorm": 0,
    "lm_head": 0,
    "transformer.h": 0,
    "transformer.ln_f": 0,
    "model.embed_tokens": 0,
    "model.layers":0,
    "model.norm":0    
}
device = "cuda" if torch.cuda.is_available() else "cpu"

# model use for inference
model_id="mychen76/mistral7b_ocr_to_json_v1"
model = AutoModelForCausalLM.from_pretrained(
    model_id, 
    trust_remote_code=True,  
    torch_dtype=torch.float16,
    quantization_config=bnb_config,
    device_map=device_map)
# tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
```